Interior Point Policy optimization under constraints
Adding the Safe RL algorithm for Constrained MDP in the Spinning up in Deep RL repository.
Installation:
- Install the spinningup package with the IPO algorithm
git clone https://github.com/hari-sikchi/spinningup
cd spinningup
pip install -e .
- Install driftgym[Environments with constraints]
git clone https://github.com/hari-sikchi/driftgym
cd driftgym
pip install -e .
Running Experiments
python -m spinup.run ipo_pytorch --env Straight-v0 --exp_name Straight-v0_with_constraints
Welcome to Spinning Up in Deep RL!
==================================
This is an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL).
For the unfamiliar: reinforcement learning (RL) is a machine learning approach for teaching agents how to solve tasks by trial and error. Deep RL refers to the combination of RL with deep learning.
This module contains a variety of helpful resources, including:
- a short introduction to RL terminology, kinds of algorithms, and basic theory,
- an essay about how to grow into an RL research role,
- a curated list of important papers organized by topic,
- a well-documented code repo of short, standalone implementations of key algorithms,
- and a few exercises to serve as warm-ups.
Get started at spinningup.openai.com!
Citing Spinning Up
If you reference or use Spinning Up in your research, please cite:
@article{SpinningUp2018,
author = {Achiam, Joshua},
title = {{Spinning Up in Deep Reinforcement Learning}},
year = {2018}
}