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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FinRL®: Financial Reinforcement Learning. 🔥
- deep-reinforcement-learning
- drl-trading-agents
- stock-trading
- drl-algorithms
A standard API for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
Infrastructure for continually self‑improving agents
An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents.
A library of reinforcement learning components and agents
A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
- reinforcement-learning
- multiagent-reinforcement-learning
- self-play
- imitation-learning
A standard API for multi-agent reinforcement learning environments, with popular reference environments and related utilities
- api
- gymnasium
- multi-agent-reinforcement-learning
- reinforcement-learning
TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
- reinforcement-learning
- tensorflow
- contextual-bandits
- bandits
PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
- pybullet
- crazyflie
- robotics
- reinforcement-learning
Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.
An experimentation and research platform to investigate the interaction of automated agents in an abstract simulated network environments.
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
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.
This trading-gym is the first trading for agent to train with episode of short term trading itself.
The official code of Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
Trading environnement for RL agents, backtesting and training.
- trading
- backtesting
- reinforcement-learning-agent
- reinforcement-learning
Build RL environments for any domain from real cases that Browser Use agents mine from the web