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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Machine learning, in numpy
- machine-learning
- neural-networks
- topic-modeling
- gaussian-mixture-models
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
- c-plus-plus
- machine-learning
- online-learning
- contextual-bandits
An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents.
A collection of environments for autonomous driving and tactical decision-making tasks
- reinforcement-learning
- autonomous-driving
- gymnasium-environment
TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
- reinforcement-learning
- tensorflow
- contextual-bandits
- bandits
An offline deep reinforcement learning library
- deep-reinforcement-learning
- deep-learning
- pytorch
- offline-rl
Computational framework for reinforcement learning in traffic control
- reinforcement-learning
- traffic-control
- benchmark
- autonomous
OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
- openai-gym
- openai-gym-environments
- robotics
- tensorflow
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
- gym
- pybullet
- reinforcement-learning
- control
This project provides a stock market environment using OpenGym with Deep Q-learning and Policy Gradient.
Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMind
An OpenAI gym wrapper for CARLA simulator
- carla-simulator
- autonomous-driving
- deep-reinforcement-learning
- imitation-learning
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.
TavernGym and TavernBench: a faithful, full-content Hearthstone Battlegrounds simulator with an RL interface, and a 960-task decision benchmark. Code and data for "Build, Don't Play".
- game-ai
- hearthstone
- reinforcement-learning
- rust
Deep Reinforcement Learning for Financial Trading using Price Trailing @ ICASSP 2019
A micro neural network multilayer perceptron for MicroPython (used on ESP32 and Pycom modules)
- micropython
- neural-network
- multilayer-perceptron
- mlp
Build RL environments for any domain from real cases that Browser Use agents mine from the web
A set of neuroevolution experiments with/towards deep networks
- rubyml
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