该存储库为OpenAI健身房环境提供了近端策略优化（PPO）的最小PyTorch实现，具有剪裁目标

# PPO-PyTorch This repository provides a Minimal PyTorch implementation of Proximal Policy Optimization (PPO) with clipped objective for OpenAI gym environments. It is primarily intended for beginners in Reinforcement Learning for understanding the PPO algorithm. It can still be used for complex environments but may require some hyperparameter-tuning or changes in the code. Modified from https://github.com/tangyudi/Ai-Learn ## Usage - To train a new network : run `PPO_continuous.py` - To train a new network : run `PPO.py` - To train a test network : run `test_continuous.py` - To train a test network : run `test.py` ## Dependencies Trained and tested on: ``` gym==0.19.0 pyglet==1.5.27 box2d box2d-kengz gym[box2d] torch==2.0.1+cu117 ``` If you still have problems, you can check `requirement.txt`. ## References - VMPO [paper](https://arxiv.org/abs/1909.12238) - [OpenAI Spinning up](https://spinningup.openai.com/en/latest/)