PyTorch implementation of Trust Region Policy Optimization

Related tags

Deep Learningreinforcement-learningdeep-learningdeep-reinforcement-learningpytorchcontinuous-controltrpomujocotrust-region-policy-optimization
Overview

PyTorch implementation of TRPO

Try my implementation of PPO (aka newer better variant of TRPO), unless you need to you TRPO for some specific reasons.

This is a PyTorch implementation of "Trust Region Policy Optimization (TRPO)".

This is code mostly ported from original implementation by John Schulman. In contrast to another implementation of TRPO in PyTorch, this implementation uses exact Hessian-vector product instead of finite differences approximation.

Contributions

Contributions are very welcome. If you know how to make this code better, don't hesitate to send a pull request.

Usage

python main.py --env-name "Reacher-v1"

Recommended hyper parameters

InvertedPendulum-v1: 5000

Reacher-v1, InvertedDoublePendulum-v1: 15000

HalfCheetah-v1, Hopper-v1, Swimmer-v1, Walker2d-v1: 25000

Ant-v1, Humanoid-v1: 50000

Results

More or less similar to the original code. Coming soon.

Todo

  • Plots.
  • Collect data in multiple threads.
Owner
Ilya Kostrikov
Post doc
Ilya Kostrikov
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