From fc473ca202a74d40de3f29cabb2ec15821baa5fd Mon Sep 17 00:00:00 2001 From: GongXudong Date: Wed, 4 Sep 2024 17:25:53 +0800 Subject: [PATCH] add FlyCraft to third party environments (#1154) Co-authored-by: Mark Towers --- docs/environments/third_party_environments.md | 49 +++++++++++-------- 1 file changed, 28 insertions(+), 21 deletions(-) diff --git a/docs/environments/third_party_environments.md b/docs/environments/third_party_environments.md index 9024caba1..7acd8855a 100644 --- a/docs/environments/third_party_environments.md +++ b/docs/environments/third_party_environments.md @@ -28,6 +28,13 @@ goal-RL ([Gymnasium-Robotics](https://robotics.farama.org/)), *If you'd like to contribute an environment, please reach out on [Discord](https://discord.gg/bnJ6kubTg6).* +### [Buffalo-Gym: Multi-Armed Bandit Gymnasium](https://github.com/foreverska/buffalo-gym) + +![Gymnasium version dependency](https://img.shields.io/badge/Gymnasium-v0.29.1-blue) +![GitHub stars](https://img.shields.io/github/stars/foreverska/buffalo-gym) + +Buffalo-Gym is a Multi-Armed Bandit (MAB) gymnasium built primarily to assist in debugging RL implementations. MABs are often easy to reason about what the agent is learning and whether it is correct. Buffalo-gym encompasses Bandits, Contextual bandits, and contextual bandits with aliasing. + ### [CARL: context adaptive RL](https://github.com/automl/CARL) ![Gymnasium version dependency](https://img.shields.io/badge/Gymnasium-v0.27.1-blue) @@ -49,6 +56,13 @@ Craftium wraps the [Minetest](https://www.minetest.net/) game engine into the Gy A benchmark library for [Dynamic Algorithm Configuration](https://www.automl.org/dynamic-algorithm-configuration/). Its focus is on reproducibility and comparability of different DAC methods as well as easy analysis of the optimization process. +### [EV2Gym: A Realistic EV-V2G-Gym Simulator for EV Smart Charging](https://github.com/StavrosOrf/EV2Gym) + +![Gymnasium version dependency](https://img.shields.io/badge/Gymnasium-v0.27.1-blue) +![GitHub stars](https://img.shields.io/github/stars/StavrosOrf/EV2Gym) + +EV2Gym is a fully customizable and easily configurable environment for Electric Vehicle (EV) smart charging simulations on a small and large scale. Also, includes non-RL baseline implementations such as mathematical programming, model predictive control, and heuristics. + ### [flappy-bird-env](https://github.com/robertoschiavone/flappy-bird-env) ![Gymnasium version dependency](https://img.shields.io/badge/Gymnasium-v0.28.1-blue) @@ -63,6 +77,13 @@ Flappy Bird as a Farama Gymnasium environment. A simple environment for single-agent reinforcement learning algorithms on a clone of [Flappy Bird](https://en.wikipedia.org/wiki/Flappy_Bird), the hugely popular arcade-style mobile game. Both state and pixel observation environments are available. +### [FlyCraft: A Fixed-wing UAV Environment](https://github.com/GongXudong/fly-craft) + +![Gymnasium version dependency](https://img.shields.io/badge/Gymnasium-v0.28.1-blue) +![GitHub stars](https://img.shields.io/github/stars/GongXudong/fly-craft) + +FlyCraft is a Gymnasium environment for fixed-wing UAV tasks. By default, FlyCraft provides two tasks: attitude control and velocity vector control. These tasks are characterized by their multi-goal and long-horizon nature, posing significant challenges for RL exploration. Additionally, the rewards can be configured as either Markovian or non-Markovian, making FlyCraft suitable for research on non-Markovian problems. + ### [gym-cellular-automata: Cellular Automata environments](https://github.com/elbecerrasoto/gym-cellular-automata) ![Gymnasium version dependency](https://img.shields.io/badge/Gymnasium-v0.28.1-blue) @@ -98,6 +119,13 @@ An environment for guiding automated theorem provers based on saturation algorit Gym Trading Env simulates stock (or crypto) market from historical data. It was designed to be fast and easily customizable. +### [ICU-Sepsis: A Benchmark MDP Built from Real Medical Data](https://github.com/icu-sepsis/icu-sepsis) + +![Gymnasium version dependency](https://img.shields.io/badge/Gymnasium-v0.28.1-blue) +![GitHub stars](https://img.shields.io/github/stars/icu-sepsis/icu-sepsis) + +ICU-Sepsis is a tabular reinforcement learning environment that simulates the treatment of sepsis in an intensive care unit (ICU). Introduced in the paper [ICU-Sepsis: A Benchmark MDP Built from Real Medical Data](https://arxiv.org/abs/2406.05646), the environment is lightweight and easy to use, yet challenging for most reinforcement learning algorithms. + ### [matrix-mdp: Easily create discrete MDPs](https://github.com/Paul-543NA/matrix-mdp-gym) ![Gymnasium version dependency](https://img.shields.io/badge/Gymnasium-v0.26.2-blue) @@ -168,27 +196,6 @@ Gymnasium wrapper for various environments in the SUMO traffic simulator. Suppor tmrl is a distributed framework for training Deep Reinforcement Learning AIs in real-time applications. It is demonstrated on the TrackMania 2020 video game. -### [EV2Gym: A Realistic EV-V2G-Gym Simulator for EV Smart Charging](https://github.com/StavrosOrf/EV2Gym) - -![Gymnasium version dependency](https://img.shields.io/badge/Gymnasium-v0.27.1-blue) -![GitHub stars](https://img.shields.io/github/stars/StavrosOrf/EV2Gym) - -EV2Gym is a fully customizable and easily configurable environment for Electric Vehicle (EV) smart charging simulations on a small and large scale. Also, includes non-RL baseline implementations such as mathematical programming, model predictive control, and heuristics. - -### [Buffalo-Gym: Multi-Armed Bandit Gymnasium](https://github.com/foreverska/buffalo-gym) - -![Gymnasium version dependency](https://img.shields.io/badge/Gymnasium-v0.29.1-blue) -![GitHub stars](https://img.shields.io/github/stars/foreverska/buffalo-gym) - -Buffalo-Gym is a Multi-Armed Bandit (MAB) gymnasium built primarily to assist in debugging RL implementations. MABs are often easy to reason about what the agent is learning and whether it is correct. Buffalo-gym encompasses Bandits, Contextual bandits, and contextual bandits with aliasing. - -### [ICU-Sepsis: A Benchmark MDP Built from Real Medical Data](https://github.com/icu-sepsis/icu-sepsis) - -![Gymnasium version dependency](https://img.shields.io/badge/Gymnasium-v0.28.1-blue) -![GitHub stars](https://img.shields.io/github/stars/icu-sepsis/icu-sepsis) - -ICU-Sepsis is a tabular reinforcement learning environment that simulates the treatment of sepsis in an intensive care unit (ICU). Introduced in the paper [ICU-Sepsis: A Benchmark MDP Built from Real Medical Data](https://arxiv.org/abs/2406.05646), the environment is lightweight and easy to use, yet challenging for most reinforcement learning algorithms. - ## Third-Party Environments using Gym There are a large number of third-party environments using various versions of [Gym](https://github.com/openai/gym).