PyTorch implementation of Constrained Policy Optimization

Last update: Dec 08, 2022

Overview

PyTorch implementation of Constrained Policy Optimization (CPO)

This repository has a simple to understand and use implementation of CPO in PyTorch. A dummy constraint function is included and can be adapted based on your needs.

Pre-requisites

PyTorch (The code is tested on PyTorch 1.2.0.)
OpenAI Gym.
MuJoCo (mujoco-py)
If working with a GPU, set OMP_NUM_THREADS to 1 using:

export OMP_NUM_THREADS=1

Features

Tensorboard integration to track learning.
Best model is tracked and saved using the value and standard deviation of average reward.

Usage

python algos/main.py --env-name CartPole-v1 --algo-name=CPO --exp-num=1 --exp-name=CPO/CartPole --save-intermediate-model=10 --gpu-index=0 --max-iter=500

Code Reference

Khrylx/PyTorch-RL

Technical Details on CPO

Owner

Sapana Chaudhary

I am a third year Ph.D. candidate in the department of Electrical and Computer Engineering at Texas A&M University.

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