Implementation of the proximal policy optimization on Mujoco environments. All hyper-parameters have been chosen based on the paper.
For Atari domain. look at this.
| Ant-v2 | Walker2d-v2 | InvertedDoublePendulum-v2 |
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| Ant-v2 | Walker2d-v2 | InvertedDoublePendulum-v2 |
|---|---|---|
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- gym == 0.17.2
- mujoco-py == 2.0.2.13
- numpy == 1.19.1
- opencv_contrib_python == 3.4.0.12
- torch == 1.4.0
pip3 install -r requirements.txtpython3 main.py- You may use
Train_FLAGflag to specify whether to train your agent when it isTrueor test it when the flag isFalse. - There are some pre-trained weights in pre-trained models dir, you can test the agent by using them; put them on the root folder of the project and turn
Train_FLAGflag toFalse.
- Ant
- InvertedDoublePendulum
- Walker2d
- Hopper
- Humanoid
- Swimmer
- HalfCheetah
Proximal Policy Optimization Algorithms, Schulman et al., 2017
- @higgsfield for his ppo code.
- @OpenAI for Baselines.
- @Reinforcement Learning KR for their Policy Gradient Algorithms.