FinRL®: Financial Reinforcement Learning. 🔥
- deep-reinforcement-learning
- drl-trading-agents
- stock-trading
- drl-algorithms
Repository Index
47,133 repositories indexed
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OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
- reinforcement-learning
- reinforcement-learning-algorithms
- machine-learning
- gym
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
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
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
- reinforcement-learning
- multiagent-reinforcement-learning
- self-play
- imitation-learning
TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
- reinforcement-learning
- tensorflow
- contextual-bandits
- bandits
A toolkit for reproducible reinforcement learning research.
- rl-algorithms
- reproducibility
- pytorch
- tensorflow
Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.
Clean PyTorch implementations of imitation and reward learning algorithms
- reward-learning
- inverse-reinforcement-learning
- imitation-learning
- gymnasium
OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
- openai-gym
- openai-gym-environments
- robotics
- tensorflow
Scalable, event-driven, deep-learning-friendly backtesting library
- reinforcement-learning
- deep-reinforcement-learning
- gym-environment
- openai-gym
A toolbox of AI modules written in Swift: Graphs/Trees, Support Vector Machines, Neural Networks, PCA, K-Means, Genetic Algorithms
Environment for reinforcement-learning algorithmic trading models
A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.
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.
Trading environnement for RL agents, backtesting and training.
- trading
- backtesting
- reinforcement-learning-agent
- reinforcement-learning
reinfore learning tool box, contains trpo, a3c algorithm for continous action space
Workbench for practical machine learning in Ruby.
- machine-learning
- machine-learning-algorithms
- neural-network
- neural-networks