There are 10 repositories under reinforcement topic.
DeepRL algorithms implementation easy for understanding and reading with Pytorch and Tensorflow 2(DQN, REINFORCE, VPG, A2C, TRPO, PPO, DDPG, TD3, SAC)
A PyTorch Library for Reinforcement Learning Research
Reinforcement learning models in ViZDoom environment
Flexible, reusable reinforcement learning (Q learning) implementation in Rust
Solvers for NP-hard and NP-complete problems with an emphasis on high-performance GPU computing.
Gym environments and agents for autonomous driving.
Contextual Bandits in R - simulation and evaluation of Multi-Armed Bandit Policies
source code for 'Improving automatic source code summarization via deep reinforcement learning'
🍄Reinforcement Learning: Super Mario Bros with dueling dqn🍄
Reinforcement learning (RL) implementation of imperfect information game Mahjong using markov decision processes to predict future game states
A course on Deep Reinforcement Learning in Computer Vision. Visit Website:
Reinforcement Workbench for FreeCAD
Autonomous Drone for Object Tracking
Playing Mountain-Car without reward engineering, by combining DQN and Random Network Distillation (RND)
[INACTIVE] A collection of various machine learning solver. The library is an object-oriented approach (baked with Typescript) and tries to deliver simplified interfaces that make using the algorithms pretty simple.
Lane keeping assistant using Reinforcement learning
Worksheet and Utilities for AWS DeepRacer – one of the most exciting ways of building strong skills in reinforcement learning and through a hands-on approach. This repository offers: 1) Functionally-rich and flexible reward function 2) Utilities with Jupiter notes for Racing Line calculation and visualisation of track 3) Scripts to parse RoboMaker training and evaluation logs to CSV file 4) Sample Excel file for car behaviour analysis as well as designing and planning new reward curves 5) Coordinates and AWS DeepRacer tracks and images.
A deep learning Crazyhouse chess program that uses a Monte Carlo Tree Search (MCTS) based evaluation system and reinforcement to enhance its play style.
Flying Cavalry Project - Ucan Kavalye Projesi
Tic Tac Toe with Alpha Zero method - My first work
Some codes used for the numerical examples proposed in https://arxiv.org/abs/1812.05916
Open AI Gym version of Berkeley AI Pacman with images as states
We use policy gradient to help agents learn optimal policies in a competitive multi-agent contextual bandit setting
本书作者是来自日本的Yutaro Ogawa(小川熊太郎),作者的github上源码是日文注释的,这个repository把它翻译成中文
Reinforcement Learning Introduction - Selected Exercise Solutions & Experiment Code
An intelligent agent that adaptively changes its thought processes to maximize cumulative reward
Robotic Arm learns to approach objects using Deep Reinforcement Learning
An implementation of a SARSA agent to learn policies in the Frozen Lake environment from OpenAI gym.