PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
- reinforcement-learning
- reinforcement-learning-algorithms
- machine-learning
- gym
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PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
An elegant PyTorch deep reinforcement learning library.
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
Deep Learning and Reinforcement Learning Library for Scientists and Engineers
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
Reinforcement Learning in PyTorch
PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
A toolkit for reproducible reinforcement learning research.
Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.
Clean PyTorch implementations of imitation and reward learning algorithms
An offline deep reinforcement learning library
C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
An Open Flexible Quadrotor Simulator
Modular Reinforcement Learning (RL) library (implemented in PyTorch, JAX, and NVIDIA Warp) with support for Gymnasium/Gym, NVIDIA Isaac Lab, MuJoCo Playground and other environments
Scalable, event-driven, deep-learning-friendly backtesting library
Gibson Environments: Real-World Perception for Embodied Agents
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
A toolbox of AI modules written in Swift: Graphs/Trees, Support Vector Machines, Neural Networks, PCA, K-Means, Genetic Algorithms
An OpenAI gym wrapper for CARLA simulator
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Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
Maze Applied Reinforcement Learning Framework
TavernGym and TavernBench: a faithful, full-content Hearthstone Battlegrounds simulator with an RL interface, and a 960-task decision benchmark. Code and data for "Build, Don't Play".
Generative models for transcriptomic-driven or protein-driven molecular design (PaccMann^RL).