FinRL®: Financial Reinforcement Learning. 🔥
- deep-reinforcement-learning
- drl-trading-agents
- stock-trading
- drl-algorithms
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FinRL®: Financial Reinforcement Learning. 🔥
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
Playing Pokemon Red with Reinforcement Learning
A toolkit for reproducible reinforcement learning research.
An experimentation and research platform to investigate the interaction of automated agents in an abstract simulated network environments.
📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.
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
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.
Reproduction of SuperMimic (arXiv:2610.28480): learned differentiable world model + BPTT policy for G1 humanoid motion tracking, on mjlab/MuJoCo Warp
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.
Communicating with godot engine through shared memory
Python application to setup and run streaming (contextual) bandit experiments.