A replica of the AlphaZero methodology for deep reinforcement learning in Python

MinRL provides clean, minimal implementations of fundamental reinforcement learning algorithms in a customizable GridWorld environment. The project focuses on educational clarity and implementation simplicity while maintaining production-quality code standards.

Machine learning library, Distributed training, Deep learning, Reinforcement learning, Models, TensorFlow, PyTorch

Pytorch realization of multiple Deep Reinforcement Learning alogrithms(DQN,DDPG,TD3,PPO,A3C...) with openai gym

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:trophy: Welcome to the wonderland of "AI" = f(DL, RL, DRL, ML, NLP, KG, MLOPS)

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一门由AlphaGo项目负责人David Sliver，在UCL(伦敦大学)主讲的强化学习经典课程

Control of 2D Rayleigh Benard Convection using Deep Reinforcement Learning with Tensorforce and Shenfun.

PyTorch implementation of classical deep reinforcement learning algorithms

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CS 294: Deep Reinforcement Learning, Spring 2017 Berkeley

A Complete Collection of Deep RL Famous Algorithms implemented in Gymnasium most Popular environments

Soft Actor-Critic (SAC) algorithm + Hindsight Experience Replay (HER) implementation on Robosuite Panda robot and Gymnasium Pick And Place (Pytorch)

My Solution to Berkeley CS285 Deep Reinforcement Learning Fall22

This is an implementation of Deep Reinforcement Learning algorithm named Deep determinsitic Policy Gradient (DDPG) to train a 4 DOF robotic Arm to reach the moving target. The action space is continous and the learned agent outputs the torque for the robot to move to the specific target location.

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Experiments with Deep Reinforcement Learning (CS 294 deep reinforcement learning fall 2017)

Examples and tutorials that implement various algorithms in Deep Reinforcement Learning.

Deep RL implementations. DQN, SAC, DDPG, TD3, PPO and VPG implemented in pytorch. Tested Env: LunarLander-v2 and Pendulum-v0.

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A DRL cheat sheet for learning

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Prototype of GPU (cuda) based Deep Reinforcement Learning code

The second project of Deep Reinforcement Learning Nanodegree

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Adaptive Portfolio Optimization with Multi-Agent Deep Reinforcement Learning

Deep Reinforcment Learning Navigation ROS2

This project demonstrates how reinforcement learning can be used to analyze market behavior and learn automated stock trading strategies. It integrates technical analysis, data visualization, and RL algorithms to produce an intelligent trading simulation.