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 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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main/environments/third_party_environments/index.html

@@ -503,6 +503,12 @@ <h2>First-Party Environments<a class="headerlink" href="#first-party-environment
 <h2>Third-party environments with Gymnasium<a class="headerlink" href="#third-party-environments-with-gymnasium" title="Link to this heading">¶</a></h2>
 <p><em>This page contains environments which are not maintained by Farama Foundation and, as such, cannot be guaranteed to function as intended.</em></p>
 <p><em>If you’d like to contribute an environment, please reach out on <a class="reference external" href="https://discord.gg/bnJ6kubTg6">Discord</a>.</em></p>
+<section id="buffalo-gym-multi-armed-bandit-gymnasium">
+<h3><a class="reference external" href="https://github.com/foreverska/buffalo-gym">Buffalo-Gym: Multi-Armed Bandit Gymnasium</a><a class="headerlink" href="#buffalo-gym-multi-armed-bandit-gymnasium" title="Link to this heading">¶</a></h3>
+<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.29.1-blue" />
+<img alt="GitHub stars" src="https://img.shields.io/github/stars/foreverska/buffalo-gym" /></p>
+<p>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.</p>
+</section>
 <section id="carl-context-adaptive-rl">
 <h3><a class="reference external" href="https://github.com/automl/CARL">CARL: context adaptive RL</a><a class="headerlink" href="#carl-context-adaptive-rl" title="Link to this heading">¶</a></h3>
 <p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.27.1-blue" />
@@ -521,6 +527,12 @@ <h3><a class="reference external" href="https://github.com/automl/DACBench">DACB
 <img alt="GitHub stars" src="https://img.shields.io/github/stars/automl/DACBench" /></p>
 <p>A benchmark library for <a class="reference external" href="https://www.automl.org/dynamic-algorithm-configuration/">Dynamic Algorithm Configuration</a>. Its focus is on reproducibility and comparability of different DAC methods as well as easy analysis of the optimization process.</p>
 </section>
+<section id="ev2gym-a-realistic-ev-v2g-gym-simulator-for-ev-smart-charging">
+<h3><a class="reference external" href="https://github.com/StavrosOrf/EV2Gym">EV2Gym: A Realistic EV-V2G-Gym Simulator for EV Smart Charging</a><a class="headerlink" href="#ev2gym-a-realistic-ev-v2g-gym-simulator-for-ev-smart-charging" title="Link to this heading">¶</a></h3>
+<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.27.1-blue" />
+<img alt="GitHub stars" src="https://img.shields.io/github/stars/StavrosOrf/EV2Gym" /></p>
+<p>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.</p>
+</section>
 <section id="flappy-bird-env">
 <h3><a class="reference external" href="https://github.com/robertoschiavone/flappy-bird-env">flappy-bird-env</a><a class="headerlink" href="#flappy-bird-env" title="Link to this heading">¶</a></h3>
 <p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.28.1-blue" />
@@ -533,6 +545,12 @@ <h3><a class="reference external" href="https://github.com/markub3327/flappy-bir
 <img alt="GitHub stars" src="https://img.shields.io/github/stars/markub3327/flappy-bird-gymnasium" /></p>
 <p>A simple environment for single-agent reinforcement learning algorithms on a clone of <a class="reference external" href="https://en.wikipedia.org/wiki/Flappy_Bird">Flappy Bird</a>, the hugely popular arcade-style mobile game. Both state and pixel observation environments are available.</p>
 </section>
+<section id="flycraft-a-fixed-wing-uav-environment">
+<h3><a class="reference external" href="https://github.com/GongXudong/fly-craft">FlyCraft: A Fixed-wing UAV Environment</a><a class="headerlink" href="#flycraft-a-fixed-wing-uav-environment" title="Link to this heading">¶</a></h3>
+<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.28.1-blue" />
+<img alt="GitHub stars" src="https://img.shields.io/github/stars/GongXudong/fly-craft" /></p>
+<p>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.</p>
+</section>
 <section id="gym-cellular-automata-cellular-automata-environments">
 <h3><a class="reference external" href="https://github.com/elbecerrasoto/gym-cellular-automata">gym-cellular-automata: Cellular Automata environments</a><a class="headerlink" href="#gym-cellular-automata-cellular-automata-environments" title="Link to this heading">¶</a></h3>
 <p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.28.1-blue" />
@@ -563,6 +581,12 @@ <h3><a class="reference external" href="https://gym-trading-env.readthedocs.io/"
 <img alt="Github stars" src="https://img.shields.io/github/stars/ClementPerroud/Gym-Trading-Env" /></p>
 <p>Gym Trading Env simulates stock (or crypto) market from historical data. It was designed to be fast and easily customizable.</p>
 </section>
+<section id="icu-sepsis-a-benchmark-mdp-built-from-real-medical-data">
+<h3><a class="reference external" href="https://github.com/icu-sepsis/icu-sepsis">ICU-Sepsis: A Benchmark MDP Built from Real Medical Data</a><a class="headerlink" href="#icu-sepsis-a-benchmark-mdp-built-from-real-medical-data" title="Link to this heading">¶</a></h3>
+<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.28.1-blue" />
+<img alt="GitHub stars" src="https://img.shields.io/github/stars/icu-sepsis/icu-sepsis" /></p>
+<p>ICU-Sepsis is a tabular reinforcement learning environment that simulates the treatment of sepsis in an intensive care unit (ICU). Introduced in the paper <a class="reference external" href="https://arxiv.org/abs/2406.05646">ICU-Sepsis: A Benchmark MDP Built from Real Medical Data</a>, the environment is lightweight and easy to use, yet challenging for most reinforcement learning algorithms.</p>
+</section>
 <section id="matrix-mdp-easily-create-discrete-mdps">
 <h3><a class="reference external" href="https://github.com/Paul-543NA/matrix-mdp-gym">matrix-mdp: Easily create discrete MDPs</a><a class="headerlink" href="#matrix-mdp-easily-create-discrete-mdps" title="Link to this heading">¶</a></h3>
 <p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.26.2-blue" />
@@ -623,24 +647,6 @@ <h3><a class="reference external" href="https://github.com/trackmania-rl/tmrl/">
 <img alt="GitHub stars" src="https://img.shields.io/github/stars/trackmania-rl/tmrl" /></p>
 <p>tmrl is a distributed framework for training Deep Reinforcement Learning AIs in real-time applications. It is demonstrated on the TrackMania 2020 video game.</p>
 </section>
-<section id="ev2gym-a-realistic-ev-v2g-gym-simulator-for-ev-smart-charging">
-<h3><a class="reference external" href="https://github.com/StavrosOrf/EV2Gym">EV2Gym: A Realistic EV-V2G-Gym Simulator for EV Smart Charging</a><a class="headerlink" href="#ev2gym-a-realistic-ev-v2g-gym-simulator-for-ev-smart-charging" title="Link to this heading">¶</a></h3>
-<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.27.1-blue" />
-<img alt="GitHub stars" src="https://img.shields.io/github/stars/StavrosOrf/EV2Gym" /></p>
-<p>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.</p>
-</section>
-<section id="buffalo-gym-multi-armed-bandit-gymnasium">
-<h3><a class="reference external" href="https://github.com/foreverska/buffalo-gym">Buffalo-Gym: Multi-Armed Bandit Gymnasium</a><a class="headerlink" href="#buffalo-gym-multi-armed-bandit-gymnasium" title="Link to this heading">¶</a></h3>
-<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.29.1-blue" />
-<img alt="GitHub stars" src="https://img.shields.io/github/stars/foreverska/buffalo-gym" /></p>
-<p>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.</p>
-</section>
-<section id="icu-sepsis-a-benchmark-mdp-built-from-real-medical-data">
-<h3><a class="reference external" href="https://github.com/icu-sepsis/icu-sepsis">ICU-Sepsis: A Benchmark MDP Built from Real Medical Data</a><a class="headerlink" href="#icu-sepsis-a-benchmark-mdp-built-from-real-medical-data" title="Link to this heading">¶</a></h3>
-<p><img alt="Gymnasium version dependency" src="https://img.shields.io/badge/Gymnasium-v0.28.1-blue" />
-<img alt="GitHub stars" src="https://img.shields.io/github/stars/icu-sepsis/icu-sepsis" /></p>
-<p>ICU-Sepsis is a tabular reinforcement learning environment that simulates the treatment of sepsis in an intensive care unit (ICU). Introduced in the paper <a class="reference external" href="https://arxiv.org/abs/2406.05646">ICU-Sepsis: A Benchmark MDP Built from Real Medical Data</a>, the environment is lightweight and easy to use, yet challenging for most reinforcement learning algorithms.</p>
-</section>
 </section>
 <section id="third-party-environments-using-gym">
 <h2>Third-Party Environments using Gym<a class="headerlink" href="#third-party-environments-using-gym" title="Link to this heading">¶</a></h2>