README.md

---
license: bsd-3-clause
tags:
- Pendulum-v1
- reinforcement-learning
- decisions
- TLA
- deep-reinforcement-learning
model-index:
- name: TLA
  results:
  - metrics:
    - type: mean_reward
      value: -154.92
      name: mean_reward
    - type: Action Repetition
      value: .7032
      name: Action Repetition
    - type: Average Decisions
      value: 62.31
      name: Average Decisions
    task:
      type: Continuous Control
      name: Continuous Control
    dataset:
      name: Pendulum-v1
      type: Pendulum-v1
  Paper: https://arxiv.org/abs/2305.18701
  Code: https://github.com/dee0512/Temporally-Layered-Architecture
---
# Temporally Layered Architecture: Pendulum-v1

These are 10 trained models over **seeds (0-9)** of **[Temporally Layered Architecture (TLA)](https://github.com/dee0512/Temporally-Layered-Architecture)** agent playing **Pendulum-v1**.

## Model Sources

**Repository:** [https://github.com/dee0512/Temporally-Layered-Architecture](https://github.com/dee0512/Temporally-Layered-Architecture)  
**Paper:** [https://doi.org/10.1162/neco_a_01718](https://doi.org/10.1162/neco_a_01718)  
**Arxiv:** [arxiv.org/abs/2305.18701](https://arxiv.org/abs/2305.18701)

# Training Details:
Using the repository:

```
python main.py --env_name <environment> --seed <seed>
```

# Evaluation:

Download the models folder and place it in the same directory as the cloned repository. 
Using the repository:

```
python eval.py --env_name <environment>
```

## Metrics:

**mean_reward:** Mean reward over 10 seeds  
**action_repeititon:** percentage of actions that are equal to the previous action  
**mean_decisions:** Number of decisions required (neural network/model forward pass)  


# Citation

The paper can be cited with the following bibtex entry:

## BibTeX:

```
@article{10.1162/neco_a_01718,
    author = {Patel, Devdhar and Sejnowski, Terrence and Siegelmann, Hava},
    title = "{Optimizing Attention and Cognitive Control Costs Using Temporally Layered Architectures}",
    journal = {Neural Computation},
    pages = {1-30},
    year = {2024},
    month = {10},
    issn = {0899-7667},
    doi = {10.1162/neco_a_01718},
    url = {https://doi.org/10.1162/neco\_a\_01718},
    eprint = {https://direct.mit.edu/neco/article-pdf/doi/10.1162/neco\_a\_01718/2474695/neco\_a\_01718.pdf},
}
```

## APA:
```
Patel, D., Sejnowski, T., & Siegelmann, H. (2024). Optimizing Attention and Cognitive Control Costs Using Temporally Layered Architectures. Neural Computation, 1-30.
```