Massively Parallel Deep Reinforcement Learning. 🔥

CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning

Related papers for reinforcement learning, including classic papers and latest papers in top conferences

PyTorch implementation of deep reinforcement learning algorithms

Solutions of assignments of Deep Reinforcement Learning course presented by the University of California, Berkeley (CS285) in Pytorch framework

A repository for implementation of deep reinforcement learning lectured at Samsung

TD3, SAC, IQN, Rainbow, PPO, Ape-X and etc. in TF1.x

Mirror Descent Policy Optimization

D2C(Data-driven Control Library) is a library for data-driven control based on reinforcement learning.

A repository for code of reinforcement learning algorithms with PyTorch

Reinforcement Learning - Implementation of Exercises, algorithms from the book Sutton Barto and David silver's RL course in Python, OpenAI Gym.

CURLA: CURL x CARLA -- Robust end-to-end Autonomous Driving by combining Contrastive Learning and Reinforcement Learning

Implementation of the CartPole from OpenAI's Gym using only visual input for Reinforcement Learning control with DQN

💡 Grasp - Pick-and-place with a robotic hand 👨🏻‍💻

Implementation of DQN and DDQN algorithms for Playing Car Racing Game

Bayesian Actor-Critic with Neural Networks. Developing an OpenAI Gym toolkit for Bayesian AC reinforcement learning.

A simple model-free and value-function-free reinforcement learning model

The repo for the FERMI FEL paper using model-based and model-free reinforcement learning methods to solve a particle accelerator operation problem.

Reinforcement Learning (RL)! This repository is your hands-on guide to implementing RL algorithms, from Markov Decision Processes (MDPs) to advanced methods like PPO and DDPG. Build smart agents, learn the math behind policies, and experiment with real-world applications!

Deep Reinforcement Learning implementation in Keras of an AI controlling the popular Flappy Bird videogame, using Asynchronous Advantage Actor Critic (A3C)