Welcome to Plangym

Welcome to Plangym#

Documentation Status Code coverage PyPI package Code style: black license: MIT

Plangym is an open source Python library for developing and comparing planning algorithms by providing a standard API to communicate between algorithms and environments, as well as a standard set of environments compliant with that API.

Given that OpenAI’s gym has become the de-facto standard in the research community, plangym’s API is designed to be as similar as possible to gym’s API while allowing to modify the environment state.

Furthermore, it provides additional functionality for stepping the environments in parallel, delayed environment initialization for dealing with environments that are difficult to serialize, compatibility with gym.Wrappers, and more.

Supported environments#

Plangym currently supports all the following environments:

  • OpenAI gym classic control environments

  • OpenAI gym Box2D environments

  • OpenAI gym Atari 2600 environments

  • Deepmind’s dm_control environments

  • Stable-retro environments

Getting started#

Stepping an environment#

import plangym
env = plangym.make(name="CartPole-v0")
state, obs, info = env.reset()

state = state.copy()
action = env.action_space.sample()

data = env.step(state=state, action=action)
new_state, observ, reward, end, truncated, info = data

Stepping a batch of states and actions#

import plangym
env = plangym.make(name="CartPole-v0")
state, obs, info = env.reset()

states = [state.copy() for _ in range(10)]
actions = [env.action_space.sample() for _ in range(10)]

data = env.step_batch(states=states, actions=actions)
new_states, observs, rewards, ends, truncateds, infos = data

Using parallel steps#

import plangym
env = plangym.make(name="MsPacman-v0", n_workers=2)

state, obs, info = env.reset()

states = [state.copy() for _ in range(10)]
actions = [env.action_space.sample() for _ in range(10)]

data =  env.step_batch(states=states, actions=actions)
new_states, observs, rewards, ends, truncateds, infos = data

Installation#

TODO: Meanwhile take a look at how we set up the repository in .github/workflows/push.yaml.

License#

Plangym is released under the MIT license.

Contributing#

Contributions are very welcome! Please check the contributing guidelines before opening a pull request.

If you have any suggestions for improvement, or you want to report a bug please open an issue.

Installing nes-py#

Step 1: Install necessary development tools and libraries#

sudo apt-get update sudo apt-get install build-essential clang sudo apt-get install libstdc+±10-dev

Step 2: Verify the compiler and include paths#

Ensure you are using g++ instead of clang++ if clang++ is not properly configured#

export CXX=g++ export CC=gcc

Rebuild the project#

rye install nes-py –git=https://github.com/FragileTech/nes-py

Introduction

User guide

API Reference

Contents