Are you sure you want to delete this task? Once this task is deleted, it cannot be recovered.
rical730 f5a0d0d0d1 | 3 years ago | |
---|---|---|
.. | ||
.benchmark | 4 years ago | |
README.md | 3 years ago | |
mujoco_agent.py | 4 years ago | |
mujoco_model.py | 4 years ago | |
train.py | 3 years ago |
Based on PARL, the SAC algorithm of deep reinforcement learning has been reproduced, reaching the same level of indicators as the paper in Mujoco benchmarks.
Include following approaches:
Paper: SAC in Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Please see here to know more about Mujoco games.
# To train an agent for HalfCheetah-v2 game
python train.py
# To train for different games
# python train.py --env [ENV_NAME]
PARL 是一个高性能、灵活的强化学习框架
Python C++ JavaScript Markdown Shell other
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》