outputs/outputs_20230421_012207/introduction.tex

\section{introduction}
Reinforcement Learning (RL) has emerged as a powerful learning paradigm for solving sequential decision-making problems, with significant advancements made in recent years due to the integration of deep neural networks \cite{2108.11510}. As a result, deep reinforcement learning has demonstrated remarkable success in various domains, including finance, medicine, healthcare, video games, robotics, and computer vision \cite{2108.11510}. However, traditional RL paradigms face challenges in modeling lifelong learning systems, which learn through trial-and-error interactions with the environment over their lifetime \cite{2001.09608}. Moreover, data inefficiency caused by trial-and-error learning mechanisms makes deep RL difficult to apply in a wide range of areas \cite{2212.00253}. This survey aims to address these challenges by exploring recent advancements in reinforcement learning, focusing on the development of more efficient and effective learning algorithms.

The problem we address is the development of more efficient and effective reinforcement learning algorithms that can learn from trial-and-error interactions with the environment, while also being able to transfer knowledge from external expertise to facilitate the learning process \cite{2009.07888}. Our proposed solution involves investigating recent advancements in RL, such as deep RL in computer vision \cite{2108.11510}, group-agent reinforcement learning \cite{2202.05135}, and distributed deep reinforcement learning \cite{2212.00253}. We aim to answer the following research questions: (1) How can we improve the efficiency and effectiveness of reinforcement learning algorithms? (2) What are the key advancements in RL that can be leveraged to address the challenges faced by traditional RL paradigms?

Related work in the field of reinforcement learning includes the development of algorithms such as Q-learning, Double Q-learning, and Dueling Q-learning \cite{2106.14642, 2106.01134, 2012.01100}. Additionally, transfer learning approaches have been explored to tackle various challenges faced by RL, by transferring knowledge from external expertise to facilitate the learning process \cite{2009.07888}. Furthermore, recent research has focused on the development of distributed deep RL algorithms, which have shown potential in various applications such as human-computer gaming and intelligent transportation \cite{2212.00253}.

Our work differs from the existing literature in that we aim to provide a comprehensive survey of the recent advancements in reinforcement learning, focusing on the development of more efficient and effective learning algorithms. By investigating various RL techniques and methodologies, we hope to identify key advancements that can be leveraged to address the challenges faced by traditional RL paradigms. Moreover, our survey will not only discuss the algorithms themselves but also explore their applications in various domains, providing a more in-depth understanding of the potential impact of these advancements on the AI community.

In conclusion, this survey will provide a detailed overview of recent advancements in reinforcement learning, with a focus on addressing the challenges faced by traditional RL paradigms and improving the efficiency and effectiveness of learning algorithms. By investigating various RL techniques and methodologies, we aim to identify key advancements that can be leveraged to address these challenges and contribute to the ongoing development of reinforcement learning as a powerful learning paradigm for solving sequential decision-making problems in various domains.