Multi-objective gym environments for reinforcement learning.

Related tags

Deep Learning reinforcement-learning gym multi-objective gym-environments morl multi-objective-rl
Overview

tests Project Status: Active – The project has reached a stable, usable state and is being actively developed. License

MO-Gym: Multi-Objective Reinforcement Learning Environments

Gym environments for multi-objective reinforcement learning (MORL). The environments follow the standard gym's API, but return vectorized rewards as numpy arrays.

For details on multi-objective MPDS (MOMDP's) and other MORL definitions, see A practical guide to multi-objective reinforcement learning and planning.

Install

git clone https://github.com/LucasAlegre/mo-gym.git
cd mo-gym
pip install -e .

Usage

import gym
import mo_gym

env = gym.make('minecart-v0') # It follows the original gym's API ...

obs = env.reset()
next_obs, vector_reward, done, info = env.step(your_agent.act(obs))  # but vector_reward is a numpy array!

# Optionally, you can scalarize the reward function with the LinearReward wrapper
env = mo_gym.LinearReward(env, weight=np.array([0.8, 0.2, 0.2]))

Environments

Env Obs/Action spaces Objectives Description
deep-sea-treasure-v0
Discrete / Discrete [treasure, time_penalty] Agent is a submarine that must collect a treasure while taking into account a time penalty. Treasures values taken from Yang et al. 2019.
resource-gathering-v0
Discrete / Discrete [enemy, gold, gem] Agent must collect gold or gem. Enemies have a 10% chance of killing the agent. From Barret & Narayanan 2008.
four-room-v0
Discrete / Discrete [item1, item2, item3] Agent must collect three different types of items in the map and reach the goal.
mo-mountaincar-v0
Continuous / Discrete [time_penalty, reverse_penalty, forward_penalty] Classic Mountain Car env, but with extra penalties for the forward and reverse actions. From Vamplew et al. 2011.
mo-reacher-v0
Continuous / Discrete [target_1, target_2, target_3, target_4] Reacher robot from PyBullet, but there are 4 different target positions.
minecart-v0
Continuous or Image / Discrete [ore1, ore2, fuel] Agent must collect two types of ores and minimize fuel consumption. From Abels et al. 2019.
mo-supermario-v0
Image / Discrete [x_pos, time, death, coin, enemy] Multi-objective version of SuperMarioBrosEnv. Objectives are defined similarly as in Yang et al. 2019.

Citing

If you use this repository in your work, please cite:

@misc{mo-gym,
  author = {Lucas N. Alegre},
  title = {MO-Gym: Multi-Objective Reinforcement Learning Environments},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/LucasAlegre/mo-gym}},
}

Acknowledgments

Comments
  • Adds the breakable bottles environment

    Adds the breakable bottles environment

    Adds the breakable bottles environment which is used in Vamplew et al. 2021 as a toy model for irreversible change in stochastic environments.

    I wasn't really planning for creating a pull request, so the commit history is a bit messy...

    opened by rk1a 4
  • A few bug fixes

    A few bug fixes

    DST:

    • The bounds of the rewards were hardcoded for the convex map.
    • The way to fix the seed is deprecated. From what I saw in the official gym envs, the seed is now fixed just using the reset method. (e.g. https://github.com/openai/gym/blob/master/gym/envs/classic_control/cartpole.py#L198)

    setup.py:

    • Gym 0.25.0 introduces breaking changes. So I fixed the version to 0.24.1.
    opened by ffelten 2
  • Consider using info field for reward vector

    Consider using info field for reward vector

    Hello,

    Thanks for this repository, it will be very useful to the MORL community :-).

    I was just wondering if you think it would be a good idea to enforce gym compatibility by specifying rewards as scalar and giving the vectorial rewards elsewhere. The idea would be to use a field in the info dictionary as they do in PGMORL. This would allow to use existing RL algorithms and logging libraries out of box (e.g. stable-baselines, tensorboard logs, ...).

    For example: In a DST env, if you return the treasure reward only in the reward field, you can use the DQN implementation from baselines and have insights on the average reward, as well as the episode length in the tensorboard logs. Of course, you can extract the full vectorial reward from the info dictionary in order to learn with MORL :-).

    With kind regards,

    Florian

    opened by ffelten 2
  • Add MO reward wrappers

    Add MO reward wrappers

    I added two wrappers commonly used: normalize and clip.

    The idea is to provide the index of the reward component you want to normalize or clip, and leave the other components as they are. Of course, wrappers can be wrapped inside others to normalize all rewards (see tests).

    opened by ffelten 1
  • Fix notebook

    Fix notebook

    There are still issues with the video recorder :(

    /usr/local/lib/python3.9/site-packages/gym/wrappers/monitoring/video_recorder.py:59: UserWarning: WARN: Disabling video recorder because environment <TimeLimit<OrderEnforcing<MOMountainCar<mo-mountaincar-v0>>>> was not initialized with any compatible video mode between `rgb_array` and `rgb_array_list`
  logger.warn(
    opened by ffelten 0
  • Add fishwood env

    Add fishwood env

    Code was provided by Denis Steckelmacher, I did a bit of refactoring and migrated it to 0.26.

    I didn't bother making the render with the images, but I did upload them in case somebody gets motivated, the env is super simple.

    opened by ffelten 0
  • Add wrapper to help logging episode returns

    Add wrapper to help logging episode returns

    The implementation is mostly a copy paste of the original gym. I had to copy paste instead of override and call to super because the way the return is a numpy array, which is mutable, and the original implementation resets it to 0. Hence, if we kept the original, the return will always be a vector of zeros (because resetted)

    opened by ffelten 0
Releases(0.2.1)
Owner
Lucas Alegre
PhD student at Institute of Informatics - UFRGS. Interested in reinforcement learning, machine learning and artificial (neuro-inspired) intelligence.
Lucas Alegre
GitHub
A general-purpose, flexible, and easy-to-use simulator alongside an OpenAI Gym trading environment for MetaTrader 5 trading platform (Approved by OpenAI Gym)

gym-mtsim: OpenAI Gym - MetaTrader 5 Simulator MtSim is a simulator for the MetaTrader 5 trading platform alongside an OpenAI Gym environment for rein

Mohammad Amin Haghpanah 184 Dec 31, 2022
[ICML 2020] Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

PG-MORL This repository contains the implementation for the paper Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Contro

MIT Graphics Group 65 Jan 7, 2023
Manipulation OpenAI Gym environments to simulate robots at the STARS lab

Manipulator Learning This repository contains a set of manipulation environments that are compatible with OpenAI Gym and simulated in pybullet. In par

STARS Laboratory 5 Dec 8, 2022
Reinforcement Learning with Q-Learning Algorithm on gym's frozen lake environment implemented in python

Reinforcement Learning with Q Learning Algorithm Q learning algorithm is trained on the gym's frozen lake environment. Libraries Used gym Numpy tqdm P

null 1 Nov 10, 2021
Trading Gym is an open source project for the development of reinforcement learning algorithms in the context of trading.

Trading Gym Trading Gym is an open-source project for the development of reinforcement learning algorithms in the context of trading. It is currently

Dimitry Foures 535 Nov 15, 2022
Plug-n-Play Reinforcement Learning in Python with OpenAI Gym and JAX

coax is built on top of JAX, but it doesn't have an explicit dependence on the jax python package. The reason is that your version of jaxlib will depend on your CUDA version.

null 128 Dec 27, 2022
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and reinforcement learning

safe-control-gym Physics-based CartPole and Quadrotor Gym environments (using PyBullet) with symbolic a priori dynamics (using CasADi) for learning-ba

Dynamic Systems Lab 300 Dec 28, 2022
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments (CoRL 2020)

Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments [Project website] [Paper] This project is a PyTorch

Cognitive Learning for Vision and Robotics (CLVR) lab @ USC 49 Nov 28, 2022
PyTorch implementations of deep reinforcement learning algorithms and environments

Deep Reinforcement Learning Algorithms with PyTorch This repository contains PyTorch implementations of deep reinforcement learning algorithms and env

Petros Christodoulou 4.7k Jan 4, 2023
CompilerGym is a library of easy to use and performant reinforcement learning environments for compiler tasks

CompilerGym is a library of easy to use and performant reinforcement learning environments for compiler tasks

Facebook Research 721 Jan 3, 2023
Exact Pareto Optimal solutions for preference based Multi-Objective Optimization

Exact Pareto Optimal solutions for preference based Multi-Objective Optimization

Debabrata Mahapatra 40 Dec 24, 2022
Code for the paper Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations (AKBC 2021).

Relation Prediction as an Auxiliary Training Objective for Knowledge Base Completion This repo provides the code for the paper Relation Prediction as

Facebook Research 85 Jan 2, 2023
Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations Requirements The code is implemented in Python and requires

null 1 Nov 3, 2021
Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX

CQL-JAX This repository implements Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX (FLAX). Implementation is built on

Karush Suri 8 Nov 7, 2022
Reinforcement-learning - Repository of the class assignment questions for the course on reinforcement learning

DSE 314/614: Reinforcement Learning This repository containing reinforcement lea

Manav Mishra 4 Apr 15, 2022
Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

30 Days Of Machine Learning Using Pytorch Objective of the repository is to learn and build machine learning models using Pytorch. List of Algorithms

Mayur 119 Nov 24, 2022
Official implementation of NeurIPS 2021 paper "One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective"

Official implementation of NeurIPS 2021 paper "One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective"

Ng Kam Woh 71 Dec 22, 2022
Pytorch implementation of "MOSNet: Deep Learning based Objective Assessment for Voice Conversion"

MOSNet pytorch implementation of "MOSNet: Deep Learning based Objective Assessment for Voice Conversion" https://arxiv.org/abs/1904.08352 Dependency L

null 9 Nov 18, 2022
CL-Gym: Full-Featured PyTorch Library for Continual Learning

CL-Gym: Full-Featured PyTorch Library for Continual Learning CL-Gym is a small yet very flexible library for continual learning research and developme

Iman Mirzadeh 36 Dec 25, 2022