42 lines
1.1 KiB
Markdown
42 lines
1.1 KiB
Markdown
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# Trust Region-Guided Proximal Policy Optimization
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Source code for the paper: [Truly Proximal Policy Optmization (TRGPPO)](https://arxiv.org/abs/1901.10314). The original code was forked from [OpenAI baselines](https://github.com/openai/baselines).
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Method is tested on [MuJoCo](http://www.mujoco.org/) continuous control tasks and [Atari](https://www.atari.com/) discrete game tasks in [OpenAI gym](https://github.com/openai/gym).
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Networks are trained using [tensorflow1.10](https://www.tensorflow.org/) and Python 3.6.
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# Installation
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```
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git clone --recursive https://github.com/wangyuhuix/TrulyPPO
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cd TrulyPPO
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pip install -r requirements.txt
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```
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# Usage
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### Command Line arguments
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* env: environment ID
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* seed: random seed
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* num_timesteps: number of timesteps
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### Continuous Task
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```shell
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python -m baselines.ppo2_AdaClip.run --alg=trulyppo --env=InvertedPendulum-v2 --seed=0
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```
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You can try `--alg=pporb` for PPO-RB and `--alg-trppo` for TR-PPO.
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### Discrete Task
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```
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python -m baselines.ppo2_AdaClip.run alg=trulyppo --env=BeamRiderNoFrameskip-v4 --seed=0 --isatari
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```
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