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---
license: bsd-3-clause
tags:
- InvertedDoublePendulum-v2
- reinforcement-learning
- decisions
- TLA
- deep-reinforcement-learning
model-index:
- name: TLA
results:
- metrics:
- type: mean_reward
value: 9356.67
name: mean_reward
- type: Action Repetition
value: .7522
name: Action Repetition
- type: Average Decisions
value: 247.76
name: Average Decisions
task:
type: OpenAI Gym
name: OpenAI Gym
dataset:
name: InvertedDoublePendulum-v2
type: InvertedDoublePendulum-v2
Paper: https://arxiv.org/abs/2305.18701
Code: https://github.com/dee0512/Temporally-Layered-Architecture
---
# Temporally Layered Architecture: InvertedDoublePendulum-v2
These are 10 trained models over **seeds (0-9)** of **[Temporally Layered Architecture (TLA)](https://github.com/dee0512/Temporally-Layered-Architecture)** agent playing **InvertedDoublePendulum-v2**.
## 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.
``` |