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README.md
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---
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license: bsd-3-clause
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---
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license: bsd-3-clause
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tags:
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- InvertedPendulum-v2
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- reinforcement-learning
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- decisions
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- TLA
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- deep-reinforcement-learning
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model-index:
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- name: TLA
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results:
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- metrics:
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- type: mean_reward
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value: 1000.00
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name: mean_reward
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- type: Action Repetition
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value: .8882
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name: Action Repetition
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- type: Average Decisions
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value: 111.79
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name: Average Decisions
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task:
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type: OpenAI Gym
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name: OpenAI Gym
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dataset:
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name: InvertedPendulum-v2
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type: InvertedPendulum-v2
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Paper: https://arxiv.org/abs/2305.18701
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Code: https://github.com/dee0512/Temporally-Layered-Architecture
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---
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# Temporally Layered Architecture: Pendulum-v1
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These are 10 trained models over **seeds (0-9)** of **[Temporally Layered Architecture (TLA)](https://github.com/dee0512/Temporally-Layered-Architecture)** agent playing **InvertedPendulum-v2**.
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## Model Sources
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**Repository:** [https://github.com/dee0512/Temporally-Layered-Architecture](https://github.com/dee0512/Temporally-Layered-Architecture)
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**Paper:** [https://doi.org/10.1162/neco_a_01718](https://doi.org/10.1162/neco_a_01718)
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**Arxiv:** [arxiv.org/abs/2305.18701](https://arxiv.org/abs/2305.18701)
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# Training Details:
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Using the repository:
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```
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python main.py --env_name <environment> --seed <seed>
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```
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# Evaluation:
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Download the models folder and place it in the same directory as the cloned repository.
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Using the repository:
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```
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python eval.py --env_name <environment>
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```
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## Metrics:
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**mean_reward:** Mean reward over 10 seeds
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**action_repeititon:** percentage of actions that are equal to the previous action
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**mean_decisions:** Number of decisions required (neural network/model forward pass)
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# Citation
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The paper can be cited with the following bibtex entry:
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## BibTeX:
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```
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@article{10.1162/neco_a_01718,
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author = {Patel, Devdhar and Sejnowski, Terrence and Siegelmann, Hava},
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title = "{Optimizing Attention and Cognitive Control Costs Using Temporally Layered Architectures}",
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journal = {Neural Computation},
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pages = {1-30},
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year = {2024},
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month = {10},
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issn = {0899-7667},
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doi = {10.1162/neco_a_01718},
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url = {https://doi.org/10.1162/neco\_a\_01718},
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eprint = {https://direct.mit.edu/neco/article-pdf/doi/10.1162/neco\_a\_01718/2474695/neco\_a\_01718.pdf},
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}
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```
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## APA:
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```
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Patel, D., Sejnowski, T., & Siegelmann, H. (2024). Optimizing Attention and Cognitive Control Costs Using Temporally Layered Architectures. Neural Computation, 1-30.
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```
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