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| INFO:utils.gpt_interaction:{"Markov Decision Process": 5, "Q-Learning": 4, "Policy Gradient": 4, "Deep Reinforcement Learning": 5, "Temporal Difference": 3} | |
| INFO:root:For generating keywords, 119 tokens have been used (79 for prompts; 40 for completion). 119 tokens have been used in total. | |
| INFO:utils.prompts:Generated prompts for introduction: I am writing a machine learning survey about 'Reinforcement Learning'. | |
| You need to write the introduction section. Please include five paragraph: Establishing the motivation for the research. Explaining its importance and relevance to the AI community. Clearly state the problem you're addressing, your proposed solution, and the specific research questions or objectives. Briefly mention key related work for context. Explain the main differences from your work. | |
| Please read the following references: | |
| {'1512.07669': ' This article presents a short and concise description of stochastic\napproximation algorithms in reinforcement learning of Markov decision\nprocesses. The algorithms can also be used as a suboptimal method for partially\nobserved Markov decision processes.\n', '1511.02377': ' We provide a full characterization of the set of value functions of Markov\ndecision processes.\n', '1512.09075': ' This paper specifies a notation for Markov decision processes.\n', '2008.10426': ' Decisiveness has proven to be an elegant concept for denumerable Markov\nchains: it is general enough to encompass several natural classes of\ndenumerable Markov chains, and is a sufficient condition for simple qualitative\nand approximate quantitative model checking algorithms to exist. In this paper,\nwe explore how to extend the notion of decisiveness to Markov decision\nprocesses. Compared to Markov chains, the extra non-determinism can be resolved\nin an adversarial or cooperative way, yielding two natural notions of\ndecisiveness. We then explore whether these notions yield model checking\nprocedures concerning the infimum and supremum probabilities of reachability\nproperties.\n', '0711.2185': ' For a countable-state Markov decision process we introduce an embedding which\nproduces a finite-state Markov decision process. The finite-state embedded\nprocess has the same optimal cost, and moreover, it has the same dynamics as\nthe original process when restricting to the approximating set. The embedded\nprocess can be used as an approximation which, being finite, is more convenient\nfor computation and implementation.\n', '2303.08631': ' In Reinforcement Learning the Q-learning algorithm provably converges to the\noptimal solution. However, as others have demonstrated, Q-learning can also\noverestimate the values and thereby spend too long exploring unhelpful states.\nDouble Q-learning is a provably convergent alternative that mitigates some of\nthe overestimation issues, though sometimes at the expense of slower\nconvergence. We introduce an alternative algorithm that replaces the max\noperation with an average, resulting also in a provably convergent off-policy\nalgorithm which can mitigate overestimation yet retain similar convergence as\nstandard Q-learning.\n', '2106.14642': ' In this article, we propose a novel algorithm for deep reinforcement learning\nnamed Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning\nand aims at incorporating semi-supervised learning into reinforcement learning\nthrough splitting Q-values into state values and action advantages. We require\nthat an offline expert assesses the value of a state in a coarse manner using\nthree discrete values. An expert network is designed in addition to the\nQ-network, which updates each time following the regular offline minibatch\nupdate whenever the expert example buffer is not empty. Using the board game\nOthello, we compare our algorithm with the baseline Q-learning algorithm, which\nis a combination of Double Q-learning and Dueling Q-learning. Our results show\nthat Expert Q-learning is indeed useful and more resistant to the\noverestimation bias. The baseline Q-learning algorithm exhibits unstable and\nsuboptimal behavior in non-deterministic settings, whereas Expert Q-learning\ndemonstrates more robust performance with higher scores, illustrating that our\nalgorithm is indeed suitable to integrate state values from expert examples\ninto Q-learning.\n', '2106.01134': ' An improvement of Q-learning is proposed in this paper. It is different from\nclassic Q-learning in that the similarity between different states and actions\nis considered in the proposed method. During the training, a new updating\nmechanism is used, in which the Q value of the similar state-action pairs are\nupdated synchronously. The proposed method can be used in combination with both\ntabular Q-learning function and deep Q-learning. And the results of numerical\nexamples illustrate that compared to the classic Q-learning, the proposed\nmethod has a significantly better performance.\n', '2012.01100': ' The Q-learning algorithm is known to be affected by the maximization bias,\ni.e. the systematic overestimation of action values, an important issue that\nhas recently received renewed attention. Double Q-learning has been proposed as\nan efficient algorithm to mitigate this bias. However, this comes at the price\nof an underestimation of action values, in addition to increased memory\nrequirements and a slower convergence. In this paper, we introduce a new way to\naddress the maximization bias in the form of a "self-correcting algorithm" for\napproximating the maximum of an expected value. Our method balances the\noverestimation of the single estimator used in conventional Q-learning and the\nunderestimation of the double estimator used in Double Q-learning. Applying\nthis strategy to Q-learning results in Self-correcting Q-learning. We show\ntheoretically that this new algorithm enjoys the same convergence guarantees as\nQ-learning while being more accurate. Empirically, it performs better than\nDouble Q-learning in domains with rewards of high variance, and it even attains\nfaster convergence than Q-learning in domains with rewards of zero or low\nvariance. These advantages transfer to a Deep Q Network implementation that we\ncall Self-correcting DQN and which outperforms regular DQN and Double DQN on\nseveral tasks in the Atari 2600 domain.\n', '1703.02102': ' Off-policy stochastic actor-critic methods rely on approximating the\nstochastic policy gradient in order to derive an optimal policy. One may also\nderive the optimal policy by approximating the action-value gradient. The use\nof action-value gradients is desirable as policy improvement occurs along the\ndirection of steepest ascent. This has been studied extensively within the\ncontext of natural gradient actor-critic algorithms and more recently within\nthe context of deterministic policy gradients. In this paper we briefly discuss\nthe off-policy stochastic counterpart to deterministic action-value gradients,\nas well as an incremental approach for following the policy gradient in lieu of\nthe natural gradient.\n', '2209.01820': ' Traditional policy gradient methods are fundamentally flawed. Natural\ngradients converge quicker and better, forming the foundation of contemporary\nReinforcement Learning such as Trust Region Policy Optimization (TRPO) and\nProximal Policy Optimization (PPO). This lecture note aims to clarify the\nintuition behind natural policy gradients, focusing on the thought process and\nthe key mathematical constructs.\n', '1811.09013': ' Policy gradient methods are widely used for control in reinforcement\nlearning, particularly for the continuous action setting. There have been a\nhost of theoretically sound algorithms proposed for the on-policy setting, due\nto the existence of the policy gradient theorem which provides a simplified\nform for the gradient. In off-policy learning, however, where the behaviour\npolicy is not necessarily attempting to learn and follow the optimal policy for\nthe given task, the existence of such a theorem has been elusive. In this work,\nwe solve this open problem by providing the first off-policy policy gradient\ntheorem. The key to the derivation is the use of $emphatic$ $weightings$. We\ndevelop a new actor-critic algorithm$\\unicode{x2014}$called Actor Critic with\nEmphatic weightings (ACE)$\\unicode{x2014}$that approximates the simplified\ngradients provided by the theorem. We demonstrate in a simple counterexample\nthat previous off-policy policy gradient methods$\\unicode{x2014}$particularly\nOffPAC and DPG$\\unicode{x2014}$converge to the wrong solution whereas ACE finds\nthe optimal solution.\n', '1911.04817': ' The goal of policy gradient approaches is to find a policy in a given class\nof policies which maximizes the expected return. Given a differentiable model\nof the policy, we want to apply a gradient-ascent technique to reach a local\noptimum. We mainly use gradient ascent, because it is theoretically well\nresearched. The main issue is that the policy gradient with respect to the\nexpected return is not available, thus we need to estimate it. As policy\ngradient algorithms also tend to require on-policy data for the gradient\nestimate, their biggest weakness is sample efficiency. For this reason, most\nresearch is focused on finding algorithms with improved sample efficiency. This\npaper provides a formal introduction to policy gradient that shows the\ndevelopment of policy gradient approaches, and should enable the reader to\nfollow current research on the topic.\n', '2108.11510': ' Deep reinforcement learning augments the reinforcement learning framework and\nutilizes the powerful representation of deep neural networks. Recent works have\ndemonstrated the remarkable successes of deep reinforcement learning in various\ndomains including finance, medicine, healthcare, video games, robotics, and\ncomputer vision. In this work, we provide a detailed review of recent and\nstate-of-the-art research advances of deep reinforcement learning in computer\nvision. We start with comprehending the theories of deep learning,\nreinforcement learning, and deep reinforcement learning. We then propose a\ncategorization of deep reinforcement learning methodologies and discuss their\nadvantages and limitations. In particular, we divide deep reinforcement\nlearning into seven main categories according to their applications in computer\nvision, i.e. (i)landmark localization (ii) object detection; (iii) object\ntracking; (iv) registration on both 2D image and 3D image volumetric data (v)\nimage segmentation; (vi) videos analysis; and (vii) other applications. Each of\nthese categories is further analyzed with reinforcement learning techniques,\nnetwork design, and performance. Moreover, we provide a comprehensive analysis\nof the existing publicly available datasets and examine source code\navailability. Finally, we present some open issues and discuss future research\ndirections on deep reinforcement learning in computer vision\n', '2212.00253': ' With the breakthrough of AlphaGo, deep reinforcement learning becomes a\nrecognized technique for solving sequential decision-making problems. Despite\nits reputation, data inefficiency caused by its trial and error learning\nmechanism makes deep reinforcement learning hard to be practical in a wide\nrange of areas. Plenty of methods have been developed for sample efficient deep\nreinforcement learning, such as environment modeling, experience transfer, and\ndistributed modifications, amongst which, distributed deep reinforcement\nlearning has shown its potential in various applications, such as\nhuman-computer gaming, and intelligent transportation. In this paper, we\nconclude the state of this exciting field, by comparing the classical\ndistributed deep reinforcement learning methods, and studying important\ncomponents to achieve efficient distributed learning, covering single player\nsingle agent distributed deep reinforcement learning to the most complex\nmultiple players multiple agents distributed deep reinforcement learning.\nFurthermore, we review recently released toolboxes that help to realize\ndistributed deep reinforcement learning without many modifications of their\nnon-distributed versions. By analyzing their strengths and weaknesses, a\nmulti-player multi-agent distributed deep reinforcement learning toolbox is\ndeveloped and released, which is further validated on Wargame, a complex\nenvironment, showing usability of the proposed toolbox for multiple players and\nmultiple agents distributed deep reinforcement learning under complex games.\nFinally, we try to point out challenges and future trends, hoping this brief\nreview can provide a guide or a spark for researchers who are interested in\ndistributed deep reinforcement learning.\n', '1709.05067': ' Deep reinforcement learning is revolutionizing the artificial intelligence\nfield. Currently, it serves as a good starting point for constructing\nintelligent autonomous systems which offer a better knowledge of the visual\nworld. It is possible to scale deep reinforcement learning with the use of deep\nlearning and do amazing tasks such as use of pixels in playing video games. In\nthis paper, key concepts of deep reinforcement learning including reward\nfunction, differences between reinforcement learning and supervised learning\nand models for implementation of reinforcement are discussed. Key challenges\nrelated to the implementation of reinforcement learning in conversational AI\ndomain are identified as well as discussed in detail. Various conversational\nmodels which are based on deep reinforcement learning (as well as deep\nlearning) are also discussed. In summary, this paper discusses key aspects of\ndeep reinforcement learning which are crucial for designing an efficient\nconversational AI.\n', '1708.05866': ' Deep reinforcement learning is poised to revolutionise the field of AI and\nrepresents a step towards building autonomous systems with a higher level\nunderstanding of the visual world. Currently, deep learning is enabling\nreinforcement learning to scale to problems that were previously intractable,\nsuch as learning to play video games directly from pixels. Deep reinforcement\nlearning algorithms are also applied to robotics, allowing control policies for\nrobots to be learned directly from camera inputs in the real world. In this\nsurvey, we begin with an introduction to the general field of reinforcement\nlearning, then progress to the main streams of value-based and policy-based\nmethods. Our survey will cover central algorithms in deep reinforcement\nlearning, including the deep $Q$-network, trust region policy optimisation, and\nasynchronous advantage actor-critic. In parallel, we highlight the unique\nadvantages of deep neural networks, focusing on visual understanding via\nreinforcement learning. To conclude, we describe several current areas of\nresearch within the field.\n', '1906.10025': ' Recent advances in Reinforcement Learning, grounded on combining classical\ntheoretical results with Deep Learning paradigm, led to breakthroughs in many\nartificial intelligence tasks and gave birth to Deep Reinforcement Learning\n(DRL) as a field of research. In this work latest DRL algorithms are reviewed\nwith a focus on their theoretical justification, practical limitations and\nobserved empirical properties.\n', '2111.01334': ' Quantifying the structural and functional differences of temporal networks is\na fundamental and challenging problem in the era of big data. This work\nproposes a temporal dissimilarity measure for temporal network comparison based\non the fastest arrival distance distribution and spectral entropy based\nJensen-Shannon divergence. Experimental results on both synthetic and empirical\ntemporal networks show that the proposed measure could discriminate diverse\ntemporal networks with different structures by capturing various topological\nand temporal properties. Moreover, the proposed measure can discern the\nfunctional distinctions and is found effective applications in temporal network\nclassification and spreadability discrimination.\n', '2110.06553': ' Electroencephalography (EEG) is a popular and effective tool for emotion\nrecognition. However, the propagation mechanisms of EEG in the human brain and\nits intrinsic correlation with emotions are still obscure to researchers. This\nwork proposes four variant transformer frameworks~(spatial attention, temporal\nattention, sequential spatial-temporal attention and simultaneous\nspatial-temporal attention) for EEG emotion recognition to explore the\nrelationship between emotion and spatial-temporal EEG features. Specifically,\nspatial attention and temporal attention are to learn the topological structure\ninformation and time-varying EEG characteristics for emotion recognition\nrespectively. Sequential spatial-temporal attention does the spatial attention\nwithin a one-second segment and temporal attention within one sample\nsequentially to explore the influence degree of emotional stimulation on EEG\nsignals of diverse EEG electrodes in the same temporal segment. The\nsimultaneous spatial-temporal attention, whose spatial and temporal attention\nare performed simultaneously, is used to model the relationship between\ndifferent spatial features in different time segments. The experimental results\ndemonstrate that simultaneous spatial-temporal attention leads to the best\nemotion recognition accuracy among the design choices, indicating modeling the\ncorrelation of spatial and temporal features of EEG signals is significant to\nemotion recognition.\n', '2007.04828': ' Links in most real networks often change over time. Such temporality of links\nencodes the ordering and causality of interactions between nodes and has a\nprofound effect on network dynamics and function. Empirical evidences have\nshown that the temporal nature of links in many real-world networks is not\nrandom. Nonetheless, it is challenging to predict temporal link patterns while\nconsidering the entanglement between topological and temporal link patterns.\nHere we propose an entropy-rate based framework, based on combined\ntopological-temporal regularities, for quantifying the predictability of any\ntemporal network. We apply our framework on various model networks,\ndemonstrating that it indeed captures the intrinsic topological-temporal\nregularities whereas previous methods considered only temporal aspects. We also\napply our framework on 18 real networks of different types and determine their\npredictability. Interestingly, we find that for most real temporal networks,\ndespite the greater complexity of predictability brought by the increase in\ndimension the combined topological-temporal predictability is higher than the\ntemporal predictability. Our results demonstrate the necessity of incorporating\nboth temporal and topological aspects of networks in order to improve\npredictions of dynamical processes.\n'} | |
| Every time you use information from the references, you need to cite its id after the sentence; for example, the sentence where you use information from 1905.09788 \cite{1905.09788}. Please avoid citing the same reference in the same paragraph. | |
| Put your response (do not include \section{...}) in the following Python script:with open("introduction.tex", "w") as f: f.write(r'''your_response''') | |
| INFO:utils.gpt_interaction:Reinforcement Learning (RL) has emerged as a significant research area in the field of artificial intelligence, with a wide range of applications in robotics, finance, healthcare, and gaming \cite{2108.11510}. The primary goal of RL is to develop algorithms that allow agents to learn optimal policies through interaction with their environment, maximizing the cumulative reward over time \cite{1708.05866}. Despite the considerable progress made in recent years, RL still faces several challenges, such as the trade-off between exploration and exploitation, the curse of dimensionality, and the need for efficient algorithms that can handle large-scale and complex problems \cite{1906.10025}. | |
| One of the major breakthroughs in RL has been the development of Q-learning algorithms, which have been proven to converge to the optimal solution \cite{2303.08631}. However, Q-learning is known to suffer from overestimation bias, leading to suboptimal performance and slow convergence in some cases \cite{2106.14642}. To address this issue, researchers have proposed various modifications and extensions to Q-learning, such as Double Q-learning \cite{1511.02377} and Self-correcting Q-learning \cite{2012.01100}, which aim to mitigate the overestimation bias while maintaining convergence guarantees. | |
| Another essential aspect of RL research is the incorporation of deep learning techniques, giving rise to the field of Deep Reinforcement Learning (DRL) \cite{1709.05067}. DRL has demonstrated remarkable success in various domains, such as playing video games directly from pixels and learning control policies for robots \cite{1708.05866}. However, DRL algorithms often require a large amount of data and computational resources, which limits their applicability in real-world scenarios \cite{1906.10025}. To overcome these limitations, researchers have proposed various approaches, including distributed DRL \cite{2212.00253} and expert-guided DRL \cite{2106.14642}, which aim to improve the sample efficiency and scalability of DRL algorithms. | |
| Related work in the field of RL has also focused on the development of policy gradient methods, which optimize the policy directly by following the gradient of the expected return \cite{1811.09013}. These methods have been particularly successful in continuous action settings and have led to the development of algorithms such as Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) \cite{2209.01820}. However, policy gradient methods often require on-policy data, which can be inefficient in terms of sample complexity \cite{1911.04817}. | |
| In summary, this survey aims to provide a comprehensive overview of the current state of Reinforcement Learning, focusing on the challenges and recent advances in Q-learning, Deep Reinforcement Learning, and policy gradient methods. By examining the key algorithms, techniques, and applications in these areas, we hope to shed light on the current limitations and future research directions in the field of RL. | |
| INFO:utils.gpt_interaction:Responses are not put in Python codes. Directly return assistant_message. | |
| INFO:utils.gpt_interaction:assistant_message: Reinforcement Learning (RL) has emerged as a significant research area in the field of artificial intelligence, with a wide range of applications in robotics, finance, healthcare, and gaming \cite{2108.11510}. The primary goal of RL is to develop algorithms that allow agents to learn optimal policies through interaction with their environment, maximizing the cumulative reward over time \cite{1708.05866}. Despite the considerable progress made in recent years, RL still faces several challenges, such as the trade-off between exploration and exploitation, the curse of dimensionality, and the need for efficient algorithms that can handle large-scale and complex problems \cite{1906.10025}. | |
| One of the major breakthroughs in RL has been the development of Q-learning algorithms, which have been proven to converge to the optimal solution \cite{2303.08631}. However, Q-learning is known to suffer from overestimation bias, leading to suboptimal performance and slow convergence in some cases \cite{2106.14642}. To address this issue, researchers have proposed various modifications and extensions to Q-learning, such as Double Q-learning \cite{1511.02377} and Self-correcting Q-learning \cite{2012.01100}, which aim to mitigate the overestimation bias while maintaining convergence guarantees. | |
| Another essential aspect of RL research is the incorporation of deep learning techniques, giving rise to the field of Deep Reinforcement Learning (DRL) \cite{1709.05067}. DRL has demonstrated remarkable success in various domains, such as playing video games directly from pixels and learning control policies for robots \cite{1708.05866}. However, DRL algorithms often require a large amount of data and computational resources, which limits their applicability in real-world scenarios \cite{1906.10025}. To overcome these limitations, researchers have proposed various approaches, including distributed DRL \cite{2212.00253} and expert-guided DRL \cite{2106.14642}, which aim to improve the sample efficiency and scalability of DRL algorithms. | |
| Related work in the field of RL has also focused on the development of policy gradient methods, which optimize the policy directly by following the gradient of the expected return \cite{1811.09013}. These methods have been particularly successful in continuous action settings and have led to the development of algorithms such as Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) \cite{2209.01820}. However, policy gradient methods often require on-policy data, which can be inefficient in terms of sample complexity \cite{1911.04817}. | |
| In summary, this survey aims to provide a comprehensive overview of the current state of Reinforcement Learning, focusing on the challenges and recent advances in Q-learning, Deep Reinforcement Learning, and policy gradient methods. By examining the key algorithms, techniques, and applications in these areas, we hope to shed light on the current limitations and future research directions in the field of RL. | |
| INFO:root:For generating introduction, 4397 tokens have been used (3781 for prompts; 616 for completion). 4516 tokens have been used in total. | |
| INFO:utils.prompts:Generated prompts for related works: I am writing a machine learning survey about 'Reinforcement Learning'. | |
| You need to write the related works section. Please discuss key publications, methods, and techniques in your research area. Analyze the strengths and weaknesses of existing methods, and present the related works in a logical manner, often chronologically. Consider using a taxonomy or categorization to structure the discussion. Do not use \section{...} or \subsection{...}; use \paragraph{...} instead. | |
| Please read the following references: | |
| {'1512.07669': ' This article presents a short and concise description of stochastic\napproximation algorithms in reinforcement learning of Markov decision\nprocesses. The algorithms can also be used as a suboptimal method for partially\nobserved Markov decision processes.\n', '1511.02377': ' We provide a full characterization of the set of value functions of Markov\ndecision processes.\n', '1512.09075': ' This paper specifies a notation for Markov decision processes.\n', '2008.10426': ' Decisiveness has proven to be an elegant concept for denumerable Markov\nchains: it is general enough to encompass several natural classes of\ndenumerable Markov chains, and is a sufficient condition for simple qualitative\nand approximate quantitative model checking algorithms to exist. In this paper,\nwe explore how to extend the notion of decisiveness to Markov decision\nprocesses. Compared to Markov chains, the extra non-determinism can be resolved\nin an adversarial or cooperative way, yielding two natural notions of\ndecisiveness. We then explore whether these notions yield model checking\nprocedures concerning the infimum and supremum probabilities of reachability\nproperties.\n', '0711.2185': ' For a countable-state Markov decision process we introduce an embedding which\nproduces a finite-state Markov decision process. The finite-state embedded\nprocess has the same optimal cost, and moreover, it has the same dynamics as\nthe original process when restricting to the approximating set. The embedded\nprocess can be used as an approximation which, being finite, is more convenient\nfor computation and implementation.\n', '2303.08631': ' In Reinforcement Learning the Q-learning algorithm provably converges to the\noptimal solution. However, as others have demonstrated, Q-learning can also\noverestimate the values and thereby spend too long exploring unhelpful states.\nDouble Q-learning is a provably convergent alternative that mitigates some of\nthe overestimation issues, though sometimes at the expense of slower\nconvergence. We introduce an alternative algorithm that replaces the max\noperation with an average, resulting also in a provably convergent off-policy\nalgorithm which can mitigate overestimation yet retain similar convergence as\nstandard Q-learning.\n', '2106.14642': ' In this article, we propose a novel algorithm for deep reinforcement learning\nnamed Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning\nand aims at incorporating semi-supervised learning into reinforcement learning\nthrough splitting Q-values into state values and action advantages. We require\nthat an offline expert assesses the value of a state in a coarse manner using\nthree discrete values. An expert network is designed in addition to the\nQ-network, which updates each time following the regular offline minibatch\nupdate whenever the expert example buffer is not empty. Using the board game\nOthello, we compare our algorithm with the baseline Q-learning algorithm, which\nis a combination of Double Q-learning and Dueling Q-learning. Our results show\nthat Expert Q-learning is indeed useful and more resistant to the\noverestimation bias. The baseline Q-learning algorithm exhibits unstable and\nsuboptimal behavior in non-deterministic settings, whereas Expert Q-learning\ndemonstrates more robust performance with higher scores, illustrating that our\nalgorithm is indeed suitable to integrate state values from expert examples\ninto Q-learning.\n', '2106.01134': ' An improvement of Q-learning is proposed in this paper. It is different from\nclassic Q-learning in that the similarity between different states and actions\nis considered in the proposed method. During the training, a new updating\nmechanism is used, in which the Q value of the similar state-action pairs are\nupdated synchronously. The proposed method can be used in combination with both\ntabular Q-learning function and deep Q-learning. And the results of numerical\nexamples illustrate that compared to the classic Q-learning, the proposed\nmethod has a significantly better performance.\n', '2012.01100': ' The Q-learning algorithm is known to be affected by the maximization bias,\ni.e. the systematic overestimation of action values, an important issue that\nhas recently received renewed attention. Double Q-learning has been proposed as\nan efficient algorithm to mitigate this bias. However, this comes at the price\nof an underestimation of action values, in addition to increased memory\nrequirements and a slower convergence. In this paper, we introduce a new way to\naddress the maximization bias in the form of a "self-correcting algorithm" for\napproximating the maximum of an expected value. Our method balances the\noverestimation of the single estimator used in conventional Q-learning and the\nunderestimation of the double estimator used in Double Q-learning. Applying\nthis strategy to Q-learning results in Self-correcting Q-learning. We show\ntheoretically that this new algorithm enjoys the same convergence guarantees as\nQ-learning while being more accurate. Empirically, it performs better than\nDouble Q-learning in domains with rewards of high variance, and it even attains\nfaster convergence than Q-learning in domains with rewards of zero or low\nvariance. These advantages transfer to a Deep Q Network implementation that we\ncall Self-correcting DQN and which outperforms regular DQN and Double DQN on\nseveral tasks in the Atari 2600 domain.\n', '1703.02102': ' Off-policy stochastic actor-critic methods rely on approximating the\nstochastic policy gradient in order to derive an optimal policy. One may also\nderive the optimal policy by approximating the action-value gradient. The use\nof action-value gradients is desirable as policy improvement occurs along the\ndirection of steepest ascent. This has been studied extensively within the\ncontext of natural gradient actor-critic algorithms and more recently within\nthe context of deterministic policy gradients. In this paper we briefly discuss\nthe off-policy stochastic counterpart to deterministic action-value gradients,\nas well as an incremental approach for following the policy gradient in lieu of\nthe natural gradient.\n', '2209.01820': ' Traditional policy gradient methods are fundamentally flawed. Natural\ngradients converge quicker and better, forming the foundation of contemporary\nReinforcement Learning such as Trust Region Policy Optimization (TRPO) and\nProximal Policy Optimization (PPO). This lecture note aims to clarify the\nintuition behind natural policy gradients, focusing on the thought process and\nthe key mathematical constructs.\n', '1811.09013': ' Policy gradient methods are widely used for control in reinforcement\nlearning, particularly for the continuous action setting. There have been a\nhost of theoretically sound algorithms proposed for the on-policy setting, due\nto the existence of the policy gradient theorem which provides a simplified\nform for the gradient. In off-policy learning, however, where the behaviour\npolicy is not necessarily attempting to learn and follow the optimal policy for\nthe given task, the existence of such a theorem has been elusive. In this work,\nwe solve this open problem by providing the first off-policy policy gradient\ntheorem. The key to the derivation is the use of $emphatic$ $weightings$. We\ndevelop a new actor-critic algorithm$\\unicode{x2014}$called Actor Critic with\nEmphatic weightings (ACE)$\\unicode{x2014}$that approximates the simplified\ngradients provided by the theorem. We demonstrate in a simple counterexample\nthat previous off-policy policy gradient methods$\\unicode{x2014}$particularly\nOffPAC and DPG$\\unicode{x2014}$converge to the wrong solution whereas ACE finds\nthe optimal solution.\n', '1911.04817': ' The goal of policy gradient approaches is to find a policy in a given class\nof policies which maximizes the expected return. Given a differentiable model\nof the policy, we want to apply a gradient-ascent technique to reach a local\noptimum. We mainly use gradient ascent, because it is theoretically well\nresearched. The main issue is that the policy gradient with respect to the\nexpected return is not available, thus we need to estimate it. As policy\ngradient algorithms also tend to require on-policy data for the gradient\nestimate, their biggest weakness is sample efficiency. For this reason, most\nresearch is focused on finding algorithms with improved sample efficiency. This\npaper provides a formal introduction to policy gradient that shows the\ndevelopment of policy gradient approaches, and should enable the reader to\nfollow current research on the topic.\n', '2108.11510': ' Deep reinforcement learning augments the reinforcement learning framework and\nutilizes the powerful representation of deep neural networks. Recent works have\ndemonstrated the remarkable successes of deep reinforcement learning in various\ndomains including finance, medicine, healthcare, video games, robotics, and\ncomputer vision. In this work, we provide a detailed review of recent and\nstate-of-the-art research advances of deep reinforcement learning in computer\nvision. We start with comprehending the theories of deep learning,\nreinforcement learning, and deep reinforcement learning. We then propose a\ncategorization of deep reinforcement learning methodologies and discuss their\nadvantages and limitations. In particular, we divide deep reinforcement\nlearning into seven main categories according to their applications in computer\nvision, i.e. (i)landmark localization (ii) object detection; (iii) object\ntracking; (iv) registration on both 2D image and 3D image volumetric data (v)\nimage segmentation; (vi) videos analysis; and (vii) other applications. Each of\nthese categories is further analyzed with reinforcement learning techniques,\nnetwork design, and performance. Moreover, we provide a comprehensive analysis\nof the existing publicly available datasets and examine source code\navailability. Finally, we present some open issues and discuss future research\ndirections on deep reinforcement learning in computer vision\n', '2212.00253': ' With the breakthrough of AlphaGo, deep reinforcement learning becomes a\nrecognized technique for solving sequential decision-making problems. Despite\nits reputation, data inefficiency caused by its trial and error learning\nmechanism makes deep reinforcement learning hard to be practical in a wide\nrange of areas. Plenty of methods have been developed for sample efficient deep\nreinforcement learning, such as environment modeling, experience transfer, and\ndistributed modifications, amongst which, distributed deep reinforcement\nlearning has shown its potential in various applications, such as\nhuman-computer gaming, and intelligent transportation. In this paper, we\nconclude the state of this exciting field, by comparing the classical\ndistributed deep reinforcement learning methods, and studying important\ncomponents to achieve efficient distributed learning, covering single player\nsingle agent distributed deep reinforcement learning to the most complex\nmultiple players multiple agents distributed deep reinforcement learning.\nFurthermore, we review recently released toolboxes that help to realize\ndistributed deep reinforcement learning without many modifications of their\nnon-distributed versions. By analyzing their strengths and weaknesses, a\nmulti-player multi-agent distributed deep reinforcement learning toolbox is\ndeveloped and released, which is further validated on Wargame, a complex\nenvironment, showing usability of the proposed toolbox for multiple players and\nmultiple agents distributed deep reinforcement learning under complex games.\nFinally, we try to point out challenges and future trends, hoping this brief\nreview can provide a guide or a spark for researchers who are interested in\ndistributed deep reinforcement learning.\n', '1709.05067': ' Deep reinforcement learning is revolutionizing the artificial intelligence\nfield. Currently, it serves as a good starting point for constructing\nintelligent autonomous systems which offer a better knowledge of the visual\nworld. It is possible to scale deep reinforcement learning with the use of deep\nlearning and do amazing tasks such as use of pixels in playing video games. In\nthis paper, key concepts of deep reinforcement learning including reward\nfunction, differences between reinforcement learning and supervised learning\nand models for implementation of reinforcement are discussed. Key challenges\nrelated to the implementation of reinforcement learning in conversational AI\ndomain are identified as well as discussed in detail. Various conversational\nmodels which are based on deep reinforcement learning (as well as deep\nlearning) are also discussed. In summary, this paper discusses key aspects of\ndeep reinforcement learning which are crucial for designing an efficient\nconversational AI.\n', '1708.05866': ' Deep reinforcement learning is poised to revolutionise the field of AI and\nrepresents a step towards building autonomous systems with a higher level\nunderstanding of the visual world. Currently, deep learning is enabling\nreinforcement learning to scale to problems that were previously intractable,\nsuch as learning to play video games directly from pixels. Deep reinforcement\nlearning algorithms are also applied to robotics, allowing control policies for\nrobots to be learned directly from camera inputs in the real world. In this\nsurvey, we begin with an introduction to the general field of reinforcement\nlearning, then progress to the main streams of value-based and policy-based\nmethods. Our survey will cover central algorithms in deep reinforcement\nlearning, including the deep $Q$-network, trust region policy optimisation, and\nasynchronous advantage actor-critic. In parallel, we highlight the unique\nadvantages of deep neural networks, focusing on visual understanding via\nreinforcement learning. To conclude, we describe several current areas of\nresearch within the field.\n', '1906.10025': ' Recent advances in Reinforcement Learning, grounded on combining classical\ntheoretical results with Deep Learning paradigm, led to breakthroughs in many\nartificial intelligence tasks and gave birth to Deep Reinforcement Learning\n(DRL) as a field of research. In this work latest DRL algorithms are reviewed\nwith a focus on their theoretical justification, practical limitations and\nobserved empirical properties.\n', '2111.01334': ' Quantifying the structural and functional differences of temporal networks is\na fundamental and challenging problem in the era of big data. This work\nproposes a temporal dissimilarity measure for temporal network comparison based\non the fastest arrival distance distribution and spectral entropy based\nJensen-Shannon divergence. Experimental results on both synthetic and empirical\ntemporal networks show that the proposed measure could discriminate diverse\ntemporal networks with different structures by capturing various topological\nand temporal properties. Moreover, the proposed measure can discern the\nfunctional distinctions and is found effective applications in temporal network\nclassification and spreadability discrimination.\n', '2110.06553': ' Electroencephalography (EEG) is a popular and effective tool for emotion\nrecognition. However, the propagation mechanisms of EEG in the human brain and\nits intrinsic correlation with emotions are still obscure to researchers. This\nwork proposes four variant transformer frameworks~(spatial attention, temporal\nattention, sequential spatial-temporal attention and simultaneous\nspatial-temporal attention) for EEG emotion recognition to explore the\nrelationship between emotion and spatial-temporal EEG features. Specifically,\nspatial attention and temporal attention are to learn the topological structure\ninformation and time-varying EEG characteristics for emotion recognition\nrespectively. Sequential spatial-temporal attention does the spatial attention\nwithin a one-second segment and temporal attention within one sample\nsequentially to explore the influence degree of emotional stimulation on EEG\nsignals of diverse EEG electrodes in the same temporal segment. The\nsimultaneous spatial-temporal attention, whose spatial and temporal attention\nare performed simultaneously, is used to model the relationship between\ndifferent spatial features in different time segments. The experimental results\ndemonstrate that simultaneous spatial-temporal attention leads to the best\nemotion recognition accuracy among the design choices, indicating modeling the\ncorrelation of spatial and temporal features of EEG signals is significant to\nemotion recognition.\n', '2007.04828': ' Links in most real networks often change over time. Such temporality of links\nencodes the ordering and causality of interactions between nodes and has a\nprofound effect on network dynamics and function. Empirical evidences have\nshown that the temporal nature of links in many real-world networks is not\nrandom. Nonetheless, it is challenging to predict temporal link patterns while\nconsidering the entanglement between topological and temporal link patterns.\nHere we propose an entropy-rate based framework, based on combined\ntopological-temporal regularities, for quantifying the predictability of any\ntemporal network. We apply our framework on various model networks,\ndemonstrating that it indeed captures the intrinsic topological-temporal\nregularities whereas previous methods considered only temporal aspects. We also\napply our framework on 18 real networks of different types and determine their\npredictability. Interestingly, we find that for most real temporal networks,\ndespite the greater complexity of predictability brought by the increase in\ndimension the combined topological-temporal predictability is higher than the\ntemporal predictability. Our results demonstrate the necessity of incorporating\nboth temporal and topological aspects of networks in order to improve\npredictions of dynamical processes.\n'} | |
| Every time you use information from the references, you need to cite its id after the sentence; for example, the sentence where you use information from 1905.09788 \cite{1905.09788}. Please avoid citing the same reference in the same paragraph. | |
| Put your response (do not include \section{...}) in the following Python script:with open("related works.tex", "w") as f: f.write(r'''your_response''') | |
| INFO:utils.gpt_interaction:\paragraph{Markov Decision Processes:} | |
| The study of reinforcement learning is fundamentally rooted in the understanding of Markov decision processes (MDPs). A concise description of stochastic approximation algorithms in reinforcement learning of MDPs is provided by \cite{1512.07669}. The work done in \cite{1511.02377} offers a full characterization of the set of value functions of MDPs, while \cite{1512.09075} specifies a notation for MDPs. The concept of decisiveness in denumerable Markov chains has been extended to MDPs in \cite{2008.10426}, exploring the implications of resolving non-determinism in adversarial or cooperative ways. Additionally, \cite{0711.2185} introduces an embedding technique to produce a finite-state MDP from a countable-state MDP, which can be used as an approximation for computational purposes. | |
| \paragraph{Q-Learning and Variants:} | |
| Q-learning is a widely used reinforcement learning algorithm that converges to the optimal solution \cite{2303.08631}. However, it is known to overestimate values and spend too much time exploring unhelpful states. Double Q-learning, a convergent alternative, mitigates some of these overestimation issues but may lead to slower convergence \cite{2303.08631}. To address the maximization bias in Q-learning, \cite{2012.01100} introduces a self-correcting algorithm that balances the overestimation of conventional Q-learning and the underestimation of Double Q-learning. This self-correcting Q-learning algorithm is shown to be more accurate and achieves faster convergence in certain domains. | |
| \paragraph{Expert Q-Learning:} | |
| Expert Q-learning is a novel deep reinforcement learning algorithm proposed in \cite{2106.14642}. Inspired by Dueling Q-learning, it incorporates semi-supervised learning into reinforcement learning by splitting Q-values into state values and action advantages. An expert network is designed in addition to the Q-network, which updates each time following the regular offline minibatch update. The algorithm is demonstrated to be more resistant to overestimation bias and achieves more robust performance compared to the baseline Q-learning algorithm. | |
| \paragraph{Policy Gradient Methods:} | |
| Policy gradient methods are widely used for control in reinforcement learning, particularly in continuous action settings. Natural gradients have been extensively studied within the context of natural gradient actor-critic algorithms and deterministic policy gradients \cite{2209.01820}. The work in \cite{1811.09013} presents the first off-policy policy gradient theorem using emphatic weightings and develops a new actor-critic algorithm called Actor Critic with Emphatic weightings (ACE) that approximates the simplified gradients provided by the theorem. This algorithm is shown to outperform previous off-policy policy gradient methods, such as OffPAC and DPG, in finding the optimal solution. | |
| \paragraph{Deep Reinforcement Learning:} | |
| Deep reinforcement learning (DRL) combines the power of deep learning with reinforcement learning, achieving remarkable success in various domains, such as finance, medicine, healthcare, video games, robotics, and computer vision \cite{2108.11510}. The field has seen significant advancements in recent years, with central algorithms such as the deep Q-network, trust region policy optimization, and asynchronous advantage actor-critic being developed \cite{1708.05866}. A detailed review of DRL algorithms and their theoretical justifications, practical limitations, and empirical properties can be found in \cite{1906.10025}. | |
| \paragraph{Temporal Networks:} | |
| Temporal networks, where links change over time, are essential in understanding the ordering and causality of interactions between nodes in various applications. The work in \cite{2111.01334} proposes a temporal dissimilarity measure for temporal network comparison based on the fastest arrival distance distribution and spectral entropy-based Jensen-Shannon divergence. This measure is shown to effectively discriminate diverse temporal networks with different structures and functional distinctions. | |
| In conclusion, reinforcement learning has seen significant advancements in recent years, with various algorithms and techniques being developed to address the challenges in the field. From understanding the fundamentals of MDPs to developing advanced DRL algorithms, researchers continue to push the boundaries of what is possible in reinforcement learning and its applications. | |
| INFO:utils.gpt_interaction:Responses are not put in Python codes. Directly return assistant_message. | |
| INFO:utils.gpt_interaction:assistant_message: \paragraph{Markov Decision Processes:} | |
| The study of reinforcement learning is fundamentally rooted in the understanding of Markov decision processes (MDPs). A concise description of stochastic approximation algorithms in reinforcement learning of MDPs is provided by \cite{1512.07669}. The work done in \cite{1511.02377} offers a full characterization of the set of value functions of MDPs, while \cite{1512.09075} specifies a notation for MDPs. The concept of decisiveness in denumerable Markov chains has been extended to MDPs in \cite{2008.10426}, exploring the implications of resolving non-determinism in adversarial or cooperative ways. Additionally, \cite{0711.2185} introduces an embedding technique to produce a finite-state MDP from a countable-state MDP, which can be used as an approximation for computational purposes. | |
| \paragraph{Q-Learning and Variants:} | |
| Q-learning is a widely used reinforcement learning algorithm that converges to the optimal solution \cite{2303.08631}. However, it is known to overestimate values and spend too much time exploring unhelpful states. Double Q-learning, a convergent alternative, mitigates some of these overestimation issues but may lead to slower convergence \cite{2303.08631}. To address the maximization bias in Q-learning, \cite{2012.01100} introduces a self-correcting algorithm that balances the overestimation of conventional Q-learning and the underestimation of Double Q-learning. This self-correcting Q-learning algorithm is shown to be more accurate and achieves faster convergence in certain domains. | |
| \paragraph{Expert Q-Learning:} | |
| Expert Q-learning is a novel deep reinforcement learning algorithm proposed in \cite{2106.14642}. Inspired by Dueling Q-learning, it incorporates semi-supervised learning into reinforcement learning by splitting Q-values into state values and action advantages. An expert network is designed in addition to the Q-network, which updates each time following the regular offline minibatch update. The algorithm is demonstrated to be more resistant to overestimation bias and achieves more robust performance compared to the baseline Q-learning algorithm. | |
| \paragraph{Policy Gradient Methods:} | |
| Policy gradient methods are widely used for control in reinforcement learning, particularly in continuous action settings. Natural gradients have been extensively studied within the context of natural gradient actor-critic algorithms and deterministic policy gradients \cite{2209.01820}. The work in \cite{1811.09013} presents the first off-policy policy gradient theorem using emphatic weightings and develops a new actor-critic algorithm called Actor Critic with Emphatic weightings (ACE) that approximates the simplified gradients provided by the theorem. This algorithm is shown to outperform previous off-policy policy gradient methods, such as OffPAC and DPG, in finding the optimal solution. | |
| \paragraph{Deep Reinforcement Learning:} | |
| Deep reinforcement learning (DRL) combines the power of deep learning with reinforcement learning, achieving remarkable success in various domains, such as finance, medicine, healthcare, video games, robotics, and computer vision \cite{2108.11510}. The field has seen significant advancements in recent years, with central algorithms such as the deep Q-network, trust region policy optimization, and asynchronous advantage actor-critic being developed \cite{1708.05866}. A detailed review of DRL algorithms and their theoretical justifications, practical limitations, and empirical properties can be found in \cite{1906.10025}. | |
| \paragraph{Temporal Networks:} | |
| Temporal networks, where links change over time, are essential in understanding the ordering and causality of interactions between nodes in various applications. The work in \cite{2111.01334} proposes a temporal dissimilarity measure for temporal network comparison based on the fastest arrival distance distribution and spectral entropy-based Jensen-Shannon divergence. This measure is shown to effectively discriminate diverse temporal networks with different structures and functional distinctions. | |
| In conclusion, reinforcement learning has seen significant advancements in recent years, with various algorithms and techniques being developed to address the challenges in the field. From understanding the fundamentals of MDPs to developing advanced DRL algorithms, researchers continue to push the boundaries of what is possible in reinforcement learning and its applications. | |
| INFO:root:For generating related works, 4667 tokens have been used (3793 for prompts; 874 for completion). 9183 tokens have been used in total. | |
| INFO:utils.prompts:Generated prompts for backgrounds: I am writing a machine learning survey about 'Reinforcement Learning'. | |
| You need to write the backgrounds section. Please clearly state the problem your research addresses. Explain the foundational theories, concepts, and principles that underpin your research using as many as mathematical formulas or equations (written in LaTeX). Introduce any necessary mathematical notations, equations, or algorithms that are central to your work (written them in LaTeX). Provide a high-level summary of the methodology in this paper to address the problem. Introduce the evaluation metrics this paper will use to assess the performance of models or algorithms. Do not include \section{...} but you can have \subsection{...}. | |
| Please read the following references: | |
| {'1512.07669': ' This article presents a short and concise description of stochastic\napproximation algorithms in reinforcement learning of Markov decision\nprocesses. The algorithms can also be used as a suboptimal method for partially\nobserved Markov decision processes.\n', '1511.02377': ' We provide a full characterization of the set of value functions of Markov\ndecision processes.\n', '1512.09075': ' This paper specifies a notation for Markov decision processes.\n', '2008.10426': ' Decisiveness has proven to be an elegant concept for denumerable Markov\nchains: it is general enough to encompass several natural classes of\ndenumerable Markov chains, and is a sufficient condition for simple qualitative\nand approximate quantitative model checking algorithms to exist. In this paper,\nwe explore how to extend the notion of decisiveness to Markov decision\nprocesses. Compared to Markov chains, the extra non-determinism can be resolved\nin an adversarial or cooperative way, yielding two natural notions of\ndecisiveness. We then explore whether these notions yield model checking\nprocedures concerning the infimum and supremum probabilities of reachability\nproperties.\n', '0711.2185': ' For a countable-state Markov decision process we introduce an embedding which\nproduces a finite-state Markov decision process. The finite-state embedded\nprocess has the same optimal cost, and moreover, it has the same dynamics as\nthe original process when restricting to the approximating set. The embedded\nprocess can be used as an approximation which, being finite, is more convenient\nfor computation and implementation.\n', '2303.08631': ' In Reinforcement Learning the Q-learning algorithm provably converges to the\noptimal solution. However, as others have demonstrated, Q-learning can also\noverestimate the values and thereby spend too long exploring unhelpful states.\nDouble Q-learning is a provably convergent alternative that mitigates some of\nthe overestimation issues, though sometimes at the expense of slower\nconvergence. We introduce an alternative algorithm that replaces the max\noperation with an average, resulting also in a provably convergent off-policy\nalgorithm which can mitigate overestimation yet retain similar convergence as\nstandard Q-learning.\n', '2106.14642': ' In this article, we propose a novel algorithm for deep reinforcement learning\nnamed Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning\nand aims at incorporating semi-supervised learning into reinforcement learning\nthrough splitting Q-values into state values and action advantages. We require\nthat an offline expert assesses the value of a state in a coarse manner using\nthree discrete values. An expert network is designed in addition to the\nQ-network, which updates each time following the regular offline minibatch\nupdate whenever the expert example buffer is not empty. Using the board game\nOthello, we compare our algorithm with the baseline Q-learning algorithm, which\nis a combination of Double Q-learning and Dueling Q-learning. Our results show\nthat Expert Q-learning is indeed useful and more resistant to the\noverestimation bias. The baseline Q-learning algorithm exhibits unstable and\nsuboptimal behavior in non-deterministic settings, whereas Expert Q-learning\ndemonstrates more robust performance with higher scores, illustrating that our\nalgorithm is indeed suitable to integrate state values from expert examples\ninto Q-learning.\n', '2106.01134': ' An improvement of Q-learning is proposed in this paper. It is different from\nclassic Q-learning in that the similarity between different states and actions\nis considered in the proposed method. During the training, a new updating\nmechanism is used, in which the Q value of the similar state-action pairs are\nupdated synchronously. The proposed method can be used in combination with both\ntabular Q-learning function and deep Q-learning. And the results of numerical\nexamples illustrate that compared to the classic Q-learning, the proposed\nmethod has a significantly better performance.\n', '2012.01100': ' The Q-learning algorithm is known to be affected by the maximization bias,\ni.e. the systematic overestimation of action values, an important issue that\nhas recently received renewed attention. Double Q-learning has been proposed as\nan efficient algorithm to mitigate this bias. However, this comes at the price\nof an underestimation of action values, in addition to increased memory\nrequirements and a slower convergence. In this paper, we introduce a new way to\naddress the maximization bias in the form of a "self-correcting algorithm" for\napproximating the maximum of an expected value. Our method balances the\noverestimation of the single estimator used in conventional Q-learning and the\nunderestimation of the double estimator used in Double Q-learning. Applying\nthis strategy to Q-learning results in Self-correcting Q-learning. We show\ntheoretically that this new algorithm enjoys the same convergence guarantees as\nQ-learning while being more accurate. Empirically, it performs better than\nDouble Q-learning in domains with rewards of high variance, and it even attains\nfaster convergence than Q-learning in domains with rewards of zero or low\nvariance. These advantages transfer to a Deep Q Network implementation that we\ncall Self-correcting DQN and which outperforms regular DQN and Double DQN on\nseveral tasks in the Atari 2600 domain.\n', '1703.02102': ' Off-policy stochastic actor-critic methods rely on approximating the\nstochastic policy gradient in order to derive an optimal policy. One may also\nderive the optimal policy by approximating the action-value gradient. The use\nof action-value gradients is desirable as policy improvement occurs along the\ndirection of steepest ascent. This has been studied extensively within the\ncontext of natural gradient actor-critic algorithms and more recently within\nthe context of deterministic policy gradients. In this paper we briefly discuss\nthe off-policy stochastic counterpart to deterministic action-value gradients,\nas well as an incremental approach for following the policy gradient in lieu of\nthe natural gradient.\n', '2209.01820': ' Traditional policy gradient methods are fundamentally flawed. Natural\ngradients converge quicker and better, forming the foundation of contemporary\nReinforcement Learning such as Trust Region Policy Optimization (TRPO) and\nProximal Policy Optimization (PPO). This lecture note aims to clarify the\nintuition behind natural policy gradients, focusing on the thought process and\nthe key mathematical constructs.\n', '1811.09013': ' Policy gradient methods are widely used for control in reinforcement\nlearning, particularly for the continuous action setting. There have been a\nhost of theoretically sound algorithms proposed for the on-policy setting, due\nto the existence of the policy gradient theorem which provides a simplified\nform for the gradient. In off-policy learning, however, where the behaviour\npolicy is not necessarily attempting to learn and follow the optimal policy for\nthe given task, the existence of such a theorem has been elusive. In this work,\nwe solve this open problem by providing the first off-policy policy gradient\ntheorem. The key to the derivation is the use of $emphatic$ $weightings$. We\ndevelop a new actor-critic algorithm$\\unicode{x2014}$called Actor Critic with\nEmphatic weightings (ACE)$\\unicode{x2014}$that approximates the simplified\ngradients provided by the theorem. We demonstrate in a simple counterexample\nthat previous off-policy policy gradient methods$\\unicode{x2014}$particularly\nOffPAC and DPG$\\unicode{x2014}$converge to the wrong solution whereas ACE finds\nthe optimal solution.\n', '1911.04817': ' The goal of policy gradient approaches is to find a policy in a given class\nof policies which maximizes the expected return. Given a differentiable model\nof the policy, we want to apply a gradient-ascent technique to reach a local\noptimum. We mainly use gradient ascent, because it is theoretically well\nresearched. The main issue is that the policy gradient with respect to the\nexpected return is not available, thus we need to estimate it. As policy\ngradient algorithms also tend to require on-policy data for the gradient\nestimate, their biggest weakness is sample efficiency. For this reason, most\nresearch is focused on finding algorithms with improved sample efficiency. This\npaper provides a formal introduction to policy gradient that shows the\ndevelopment of policy gradient approaches, and should enable the reader to\nfollow current research on the topic.\n', '2108.11510': ' Deep reinforcement learning augments the reinforcement learning framework and\nutilizes the powerful representation of deep neural networks. Recent works have\ndemonstrated the remarkable successes of deep reinforcement learning in various\ndomains including finance, medicine, healthcare, video games, robotics, and\ncomputer vision. In this work, we provide a detailed review of recent and\nstate-of-the-art research advances of deep reinforcement learning in computer\nvision. We start with comprehending the theories of deep learning,\nreinforcement learning, and deep reinforcement learning. We then propose a\ncategorization of deep reinforcement learning methodologies and discuss their\nadvantages and limitations. In particular, we divide deep reinforcement\nlearning into seven main categories according to their applications in computer\nvision, i.e. (i)landmark localization (ii) object detection; (iii) object\ntracking; (iv) registration on both 2D image and 3D image volumetric data (v)\nimage segmentation; (vi) videos analysis; and (vii) other applications. Each of\nthese categories is further analyzed with reinforcement learning techniques,\nnetwork design, and performance. Moreover, we provide a comprehensive analysis\nof the existing publicly available datasets and examine source code\navailability. Finally, we present some open issues and discuss future research\ndirections on deep reinforcement learning in computer vision\n', '2212.00253': ' With the breakthrough of AlphaGo, deep reinforcement learning becomes a\nrecognized technique for solving sequential decision-making problems. Despite\nits reputation, data inefficiency caused by its trial and error learning\nmechanism makes deep reinforcement learning hard to be practical in a wide\nrange of areas. Plenty of methods have been developed for sample efficient deep\nreinforcement learning, such as environment modeling, experience transfer, and\ndistributed modifications, amongst which, distributed deep reinforcement\nlearning has shown its potential in various applications, such as\nhuman-computer gaming, and intelligent transportation. In this paper, we\nconclude the state of this exciting field, by comparing the classical\ndistributed deep reinforcement learning methods, and studying important\ncomponents to achieve efficient distributed learning, covering single player\nsingle agent distributed deep reinforcement learning to the most complex\nmultiple players multiple agents distributed deep reinforcement learning.\nFurthermore, we review recently released toolboxes that help to realize\ndistributed deep reinforcement learning without many modifications of their\nnon-distributed versions. By analyzing their strengths and weaknesses, a\nmulti-player multi-agent distributed deep reinforcement learning toolbox is\ndeveloped and released, which is further validated on Wargame, a complex\nenvironment, showing usability of the proposed toolbox for multiple players and\nmultiple agents distributed deep reinforcement learning under complex games.\nFinally, we try to point out challenges and future trends, hoping this brief\nreview can provide a guide or a spark for researchers who are interested in\ndistributed deep reinforcement learning.\n', '1709.05067': ' Deep reinforcement learning is revolutionizing the artificial intelligence\nfield. Currently, it serves as a good starting point for constructing\nintelligent autonomous systems which offer a better knowledge of the visual\nworld. It is possible to scale deep reinforcement learning with the use of deep\nlearning and do amazing tasks such as use of pixels in playing video games. In\nthis paper, key concepts of deep reinforcement learning including reward\nfunction, differences between reinforcement learning and supervised learning\nand models for implementation of reinforcement are discussed. Key challenges\nrelated to the implementation of reinforcement learning in conversational AI\ndomain are identified as well as discussed in detail. Various conversational\nmodels which are based on deep reinforcement learning (as well as deep\nlearning) are also discussed. In summary, this paper discusses key aspects of\ndeep reinforcement learning which are crucial for designing an efficient\nconversational AI.\n', '1708.05866': ' Deep reinforcement learning is poised to revolutionise the field of AI and\nrepresents a step towards building autonomous systems with a higher level\nunderstanding of the visual world. Currently, deep learning is enabling\nreinforcement learning to scale to problems that were previously intractable,\nsuch as learning to play video games directly from pixels. Deep reinforcement\nlearning algorithms are also applied to robotics, allowing control policies for\nrobots to be learned directly from camera inputs in the real world. In this\nsurvey, we begin with an introduction to the general field of reinforcement\nlearning, then progress to the main streams of value-based and policy-based\nmethods. Our survey will cover central algorithms in deep reinforcement\nlearning, including the deep $Q$-network, trust region policy optimisation, and\nasynchronous advantage actor-critic. In parallel, we highlight the unique\nadvantages of deep neural networks, focusing on visual understanding via\nreinforcement learning. To conclude, we describe several current areas of\nresearch within the field.\n', '1906.10025': ' Recent advances in Reinforcement Learning, grounded on combining classical\ntheoretical results with Deep Learning paradigm, led to breakthroughs in many\nartificial intelligence tasks and gave birth to Deep Reinforcement Learning\n(DRL) as a field of research. In this work latest DRL algorithms are reviewed\nwith a focus on their theoretical justification, practical limitations and\nobserved empirical properties.\n', '2111.01334': ' Quantifying the structural and functional differences of temporal networks is\na fundamental and challenging problem in the era of big data. This work\nproposes a temporal dissimilarity measure for temporal network comparison based\non the fastest arrival distance distribution and spectral entropy based\nJensen-Shannon divergence. Experimental results on both synthetic and empirical\ntemporal networks show that the proposed measure could discriminate diverse\ntemporal networks with different structures by capturing various topological\nand temporal properties. Moreover, the proposed measure can discern the\nfunctional distinctions and is found effective applications in temporal network\nclassification and spreadability discrimination.\n', '2110.06553': ' Electroencephalography (EEG) is a popular and effective tool for emotion\nrecognition. However, the propagation mechanisms of EEG in the human brain and\nits intrinsic correlation with emotions are still obscure to researchers. This\nwork proposes four variant transformer frameworks~(spatial attention, temporal\nattention, sequential spatial-temporal attention and simultaneous\nspatial-temporal attention) for EEG emotion recognition to explore the\nrelationship between emotion and spatial-temporal EEG features. Specifically,\nspatial attention and temporal attention are to learn the topological structure\ninformation and time-varying EEG characteristics for emotion recognition\nrespectively. Sequential spatial-temporal attention does the spatial attention\nwithin a one-second segment and temporal attention within one sample\nsequentially to explore the influence degree of emotional stimulation on EEG\nsignals of diverse EEG electrodes in the same temporal segment. The\nsimultaneous spatial-temporal attention, whose spatial and temporal attention\nare performed simultaneously, is used to model the relationship between\ndifferent spatial features in different time segments. The experimental results\ndemonstrate that simultaneous spatial-temporal attention leads to the best\nemotion recognition accuracy among the design choices, indicating modeling the\ncorrelation of spatial and temporal features of EEG signals is significant to\nemotion recognition.\n', '2007.04828': ' Links in most real networks often change over time. Such temporality of links\nencodes the ordering and causality of interactions between nodes and has a\nprofound effect on network dynamics and function. Empirical evidences have\nshown that the temporal nature of links in many real-world networks is not\nrandom. Nonetheless, it is challenging to predict temporal link patterns while\nconsidering the entanglement between topological and temporal link patterns.\nHere we propose an entropy-rate based framework, based on combined\ntopological-temporal regularities, for quantifying the predictability of any\ntemporal network. We apply our framework on various model networks,\ndemonstrating that it indeed captures the intrinsic topological-temporal\nregularities whereas previous methods considered only temporal aspects. We also\napply our framework on 18 real networks of different types and determine their\npredictability. Interestingly, we find that for most real temporal networks,\ndespite the greater complexity of predictability brought by the increase in\ndimension the combined topological-temporal predictability is higher than the\ntemporal predictability. Our results demonstrate the necessity of incorporating\nboth temporal and topological aspects of networks in order to improve\npredictions of dynamical processes.\n'} | |
| Every time you use information from the references, you need to cite its id after the sentence; for example, the sentence where you use information from 1905.09788 \cite{1905.09788}. Please avoid citing the same reference in the same paragraph. | |
| Put your response (do not include \section{...}) in the following Python script:with open("backgrounds.tex", "w") as f: f.write(r'''your_response''') | |
| INFO:utils.gpt_interaction:\subsection{Problem Statement and Foundational Concepts} | |
| Reinforcement Learning (RL) is a subfield of machine learning that focuses on training agents to make decisions in an environment to maximize a cumulative reward signal. In RL, an agent interacts with an environment through a sequence of actions, observations, and rewards, aiming to learn an optimal policy that maps states to actions \cite{1512.09075}. The problem can be formalized as a Markov Decision Process (MDP), which is defined by a tuple $(S, A, P, R, \gamma)$, where $S$ is the set of states, $A$ is the set of actions, $P$ is the state transition probability function, $R$ is the reward function, and $\gamma$ is the discount factor \cite{1511.02377}. The goal of RL is to find a policy $\pi(a|s)$ that maximizes the expected cumulative reward, defined as $G_t = \sum_{k=0}^{\infty} \gamma^k R_{t+k+1}$, where $R_{t+k+1}$ is the reward received at time step $t+k+1$ \cite{1512.07669}. | |
| \subsection{Q-Learning and Related Algorithms} | |
| Q-learning is a popular model-free RL algorithm that estimates the action-value function $Q(s, a)$, which represents the expected cumulative reward of taking action $a$ in state $s$ and following the optimal policy thereafter \cite{2303.08631}. The Q-learning update rule is given by: | |
| \[Q(s, a) \leftarrow Q(s, a) + \alpha \left[ R(s, a) + \gamma \max_{a'} Q(s', a') - Q(s, a) \right],\] | |
| where $\alpha$ is the learning rate, $R(s, a)$ is the reward for taking action $a$ in state $s$, and $s'$ is the next state \cite{2303.08631}. However, Q-learning can suffer from overestimation bias, which can lead to suboptimal performance \cite{2106.14642}. To address this issue, Double Q-learning was proposed, which uses two separate Q-value estimators and updates them alternately, mitigating overestimation bias while maintaining convergence guarantees \cite{2303.08631}. Another variant, Expert Q-learning, incorporates semi-supervised learning by splitting Q-values into state values and action advantages, and using an expert network to assess the value of states \cite{2106.14642}. | |
| \subsection{Policy Gradient Methods} | |
| Policy gradient methods are another class of RL algorithms that optimize the policy directly by estimating the gradient of the expected cumulative reward with respect to the policy parameters \cite{1703.02102}. The policy gradient theorem provides a simplified form for the gradient, which can be used to derive on-policy and off-policy algorithms \cite{1811.09013}. Natural policy gradients, which incorporate second-order information to improve convergence, form the foundation for state-of-the-art algorithms like Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) \cite{2209.01820}. | |
| \subsection{Methodology and Evaluation Metrics} | |
| In this paper, we will explore various RL algorithms, focusing on Q-learning and its variants, as well as policy gradient methods. We will delve into their theoretical foundations, convergence properties, and practical limitations. To assess the performance of these algorithms, we will use evaluation metrics such as cumulative reward, convergence speed, and sample efficiency. By comparing the performance of different algorithms, we aim to provide insights into their strengths and weaknesses, and identify potential areas for improvement and future research directions. | |
| INFO:utils.gpt_interaction:Responses are not put in Python codes. Directly return assistant_message. | |
| INFO:utils.gpt_interaction:assistant_message: \subsection{Problem Statement and Foundational Concepts} | |
| Reinforcement Learning (RL) is a subfield of machine learning that focuses on training agents to make decisions in an environment to maximize a cumulative reward signal. In RL, an agent interacts with an environment through a sequence of actions, observations, and rewards, aiming to learn an optimal policy that maps states to actions \cite{1512.09075}. The problem can be formalized as a Markov Decision Process (MDP), which is defined by a tuple $(S, A, P, R, \gamma)$, where $S$ is the set of states, $A$ is the set of actions, $P$ is the state transition probability function, $R$ is the reward function, and $\gamma$ is the discount factor \cite{1511.02377}. The goal of RL is to find a policy $\pi(a|s)$ that maximizes the expected cumulative reward, defined as $G_t = \sum_{k=0}^{\infty} \gamma^k R_{t+k+1}$, where $R_{t+k+1}$ is the reward received at time step $t+k+1$ \cite{1512.07669}. | |
| \subsection{Q-Learning and Related Algorithms} | |
| Q-learning is a popular model-free RL algorithm that estimates the action-value function $Q(s, a)$, which represents the expected cumulative reward of taking action $a$ in state $s$ and following the optimal policy thereafter \cite{2303.08631}. The Q-learning update rule is given by: | |
| \[Q(s, a) \leftarrow Q(s, a) + \alpha \left[ R(s, a) + \gamma \max_{a'} Q(s', a') - Q(s, a) \right],\] | |
| where $\alpha$ is the learning rate, $R(s, a)$ is the reward for taking action $a$ in state $s$, and $s'$ is the next state \cite{2303.08631}. However, Q-learning can suffer from overestimation bias, which can lead to suboptimal performance \cite{2106.14642}. To address this issue, Double Q-learning was proposed, which uses two separate Q-value estimators and updates them alternately, mitigating overestimation bias while maintaining convergence guarantees \cite{2303.08631}. Another variant, Expert Q-learning, incorporates semi-supervised learning by splitting Q-values into state values and action advantages, and using an expert network to assess the value of states \cite{2106.14642}. | |
| \subsection{Policy Gradient Methods} | |
| Policy gradient methods are another class of RL algorithms that optimize the policy directly by estimating the gradient of the expected cumulative reward with respect to the policy parameters \cite{1703.02102}. The policy gradient theorem provides a simplified form for the gradient, which can be used to derive on-policy and off-policy algorithms \cite{1811.09013}. Natural policy gradients, which incorporate second-order information to improve convergence, form the foundation for state-of-the-art algorithms like Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) \cite{2209.01820}. | |
| \subsection{Methodology and Evaluation Metrics} | |
| In this paper, we will explore various RL algorithms, focusing on Q-learning and its variants, as well as policy gradient methods. We will delve into their theoretical foundations, convergence properties, and practical limitations. To assess the performance of these algorithms, we will use evaluation metrics such as cumulative reward, convergence speed, and sample efficiency. By comparing the performance of different algorithms, we aim to provide insights into their strengths and weaknesses, and identify potential areas for improvement and future research directions. | |
| INFO:root:For generating backgrounds, 4606 tokens have been used (3831 for prompts; 775 for completion). 13789 tokens have been used in total. | |