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.
Deep Learning and Reinforcement Learning Library for Scientists and Engineers
PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.
C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
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
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
[ICRA] Crowd-aware Robot Navigation
ROS package for dynamic obstacle avoidance for ground robots trained with deep RL
Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.
Official code for "Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models" (ICRA 2026): Flow Policy Optimization for pi0