CongWeilin / PPO-PyTorch

Simple and beginner friendly implementation of Proximal Policy Optimization (PPO) in PyTorch

PPO-PyTorch

Simple and beginner friendly PyTorch implementation of Proximal Policy Optimization with clipped objective for OpenAI gym environment.

Usage

• To test a preTrained network : run test.py
• If you are trying to train it on a environment where action dimension = 1, make sure to check the tensor dimensions while updating in the update function, since I have used torch.squeeze() quite a few times. torch.squeeze() squeezes the tensor such that there are no dimensions of length = 1.(more info)

Dependencies

Trained and tested on:

Python 3.6
PyTorch 1.0
NumPy 1.15.3
gym 0.10.8
Pillow 5.3.0

Results

PPO Discrete LunarLander-v2 (1200 episodes)