PyTorch implementation of Constrained Policy Optimization

Related tags

Deep Learningreinforcement-learningdeep-reinforcement-learningpytorchconstrained-optimizationpolicy-gradientreinforcement-learning-algorithmstrpopytorch-rltrpo-pytorchpytorch-reinforcement-learningconstr
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

  1. Tensorboard integration to track learning.
  2. 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

Technical Details on CPO

main feasible infeasible

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