The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
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A standard API for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
A standard API for multi-agent reinforcement learning environments, with popular reference environments and related utilities
- api
- gymnasium
- multi-agent-reinforcement-learning
- reinforcement-learning
A collection of environments for autonomous driving and tactical decision-making tasks
- reinforcement-learning
- autonomous-driving
- gymnasium-environment
PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
- pybullet
- crazyflie
- robotics
- reinforcement-learning
Reinforcement Learning environments based on the 1993 game Doom :godmode:
- deep-learning
- reinforcement-learning
- python
- vizdoom
An experimentation and research platform to investigate the interaction of automated agents in an abstract simulated network environments.
C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
- reinforcement-learning
- parallel-processing
- cpp17
- pybind11
OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
- openai-gym
- openai-gym-environments
- robotics
- tensorflow
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
- reinforcement-learning
- python
- deep-learning
- gym
An engine for high performance multi-agent environments with very large numbers of agents, along with a set of reference environments
Gibson Environments: Real-World Perception for Embodied Agents
- computer-vision
- robotics
- simulator
- sim2real
PettingZoo and Gymnasium bindings for popular reinforcement learning environments outside of Farama
- deepmind-control-suite
- deepmind-lab
- gymnasium
- reinforcement-learning
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
- gym
- pybullet
- reinforcement-learning
- control
Build RL environments for any domain from real cases that Browser Use agents mine from the web