metadata
license: bsd-3-clause
tags:
- Pendulum-v1
- reinforcement-learning
- Soft Actor Critic
- SRL
- deep-reinforcement-learning
model-index:
- name: SAC
results:
- metrics:
- type: FAS (J=1)
value: 0.4419 ± 0.025996
name: FAS
- type: FAS (J=2)
value: 0.423547 ± 0.026536
name: FAS
- type: FAS (J=4)
value: 0.497902 ± 0.034868
name: FAS
- type: FAS (J=8)
value: 0.489516 ± 0.044905
name: FAS
- type: FAS (J=16)
value: 0.32623 ± 0.053239
name: FAS
task:
type: OpenAI Gym
name: OpenAI Gym
dataset:
name: Pendulum-v1
type: Pendulum-v1
Paper: https://arxiv.org/pdf/2410.08979
Code: https://github.com/dee0512/Sequence-Reinforcement-Learning
Soft-Actor-Critic: Pendulum-v1
These are 25 trained models over seeds (0-4) and J = 1, 2, 4, 8, 16 of Soft actor critic agent playing Pendulum-v1 for Sequence Reinforcement Learning (SRL).
Model Sources
Repository: https://github.com/dee0512/Sequence-Reinforcement-Learning
Paper (ICLR): https://openreview.net/forum?id=w3iM4WLuvy
Arxiv: arxiv.org/pdf/2410.08979
Training Details:
Using the repository:
python .\train_sac.py --env_name <env_name> --seed <seed> --j <j>
Evaluation:
Download the models folder and place it in the same directory as the cloned repository. Using the repository:
python .\eval_sac.py --env_name <env_name> --seed <seed> --j <j>
Metrics:
FAS: Frequency Averaged Score
j: Action repetition parameter
Citation
The paper can be cited with the following bibtex entry:
BibTeX:
@inproceedings{DBLP:conf/iclr/PatelS25,
author = {Devdhar Patel and
Hava T. Siegelmann},
title = {Overcoming Slow Decision Frequencies in Continuous Control: Model-Based
Sequence Reinforcement Learning for Model-Free Control},
booktitle = {The Thirteenth International Conference on Learning Representations,
{ICLR} 2025, Singapore, April 24-28, 2025},
publisher = {OpenReview.net},
year = {2025},
url = {https://openreview.net/forum?id=w3iM4WLuvy}
}
APA:
Patel, D., & Siegelmann, H. T. Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free Control. In The Thirteenth International Conference on Learning Representations.