MASA-Safe-RL: A Safe Reinforcement Library for providing a common interface for different constraints and environments
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Updated
Dec 18, 2025 - Python
MASA-Safe-RL: A Safe Reinforcement Library for providing a common interface for different constraints and environments
🎮 Train a Deep Q-Learning agent using TensorFlow to master Atari Breakout with efficient experience replay and modular architecture for easy customization.
⛰ A professional, modular reinforcement learning implementation that solves the classic MountainCar-v0 environment from OpenAI Gymnasium using Q-Learning algorithm.
🔋 Optimize battery dispatch using a rainflow-aware DQN that tracks degradation and enhances performance with advanced features for reliable results.
The Tactile-MNIST Active Perception Benchmark
Modular Reinforcement Learning (RL) library (implemented in PyTorch, JAX, and NVIDIA Warp) with support for Gymnasium/Gym, NVIDIA Isaac Lab, Brax and other environments
A Gymnasium compatible Reinforcement Learning Framework for Trackmania Nations Forever
Turned based strategy game with the goal of developing reinforcement learning algorithms
Reinforcement Learning Environment with Gymnasium API
StalkerDroneRL - ROS 2 + Gazebo + Gymnasium + Stable Baselines3
Enables you to convert a PettingZoo environment to a Gym environment while supporting multiple agents (MARL). Gym's default setup doesn't easily support multi-agent environments, but this wrapper resolves that by running each agent in its own process and sharing the environment across those processes.
A framework for modeling non-stationary Markov decision processes and the key decision making problems in these environments
A collection of robotics simulation environments for reinforcement learning
Causal RL Algortihms
Gymnasium-based Sudoku environment for reinforcement learning research/experiments
Modular JAX-based toolbox for implementing RL algorithms.
Clean RL algorithm implementations in under 100 lines each.
A Reinforcement Learning (PPO) based Statistical Arbitrage bot for Pairs Trading. Utilizes Engle-Granger cointegration tests and custom Gymnasium environments to optimize for risk-adjusted returns (Sharpe Ratio).
Playing Pokemon Red with Reinforcement Learning
Implementations of CarRacing Gymnasium
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