File size: 2,493 Bytes
228aaff 593ba93 8072f2b 593ba93 228aaff ecc917a 228aaff 2af43cc ecc917a 2af43cc 228aaff 2af43cc 228aaff 79d5454 228aaff 80310c4 228aaff 8072f2b 7eac7db 9b752a8 8072f2b e6742d1 8072f2b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
---
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: OpenAI Gym
name: OpenAI Gym
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.
``` |