Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
- wandb
- reinforcement-learning
- pytorch
- python
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
- pytorch
- reinforcement-learning
- deep-learning
- deep-reinforcement-learning
The next generation deep reinforcement learning tookit
- reinforcement-learning
- transfer-learning
- deep-learning
Modularized Implementation of Deep RL Algorithms in PyTorch
- pytorch
- deep-reinforcement-learning
- dqn
- dueling-network-architecture
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
- rl
- reinforcement-learning
- stable-baselines
- openai
A PyTorch Library for Meta-learning Research
- pytorch
- maml
- meta-learning
- learning2learn
Bayesian optimisation & Reinforcement Learning library developed by Huawei Noah's Ark Lab
verl-agent is an extension of veRL, designed for training LLM/VLM agents via RL. verl-agent is also the official code for paper "Group-in-Group Policy Optimization for LLM Agent Training"
- llm-agents
- llm-training
- reinforcement-learning
- large-language-models
Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
- pytorch-implmention
- pytorch
- pytorch-tutorial
- proximal-policy-optimization
PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
- pybullet
- crazyflie
- robotics
- reinforcement-learning
Scalable, event-driven, deep-learning-friendly backtesting library
- reinforcement-learning
- deep-reinforcement-learning
- gym-environment
- openai-gym
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
- gym
- pybullet
- reinforcement-learning
- control
A brain that learns from live experience. System 1 by default: perception, memory, plasticity and action. Optional System 2 for self-observation and slow corrections.
- machine-learning
- embodied-ai
- online-learning
- python
Maze Applied Reinforcement Learning Framework
Trading Environment(OpenAI Gym) + PPO(TensorForce)
- ppo
- proximal-policy-optimization
- tensorforce
- trading
Official code for "Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models" (ICRA 2026): Flow Policy Optimization for pi0
- aloha
- flow-matching
- icra2026
- jax
Workbench for practical machine learning in Ruby.
- machine-learning
- machine-learning-algorithms
- neural-network
- neural-networks