deep-reinforcement-learning

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

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.

Open-source simulator for autonomous driving research.

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

(⌐■_■) - Deep Reinforcement Learning instrumenting bettercap for WiFi pwning.

Lab Materials for MIT 6.S191: Introduction to Deep Learning

Trax — Deep Learning with Clear Code and Speed

Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).

A course in reinforcement learning in the wild

This repo contains the Hugging Face Deep Reinforcement Learning Course.

Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

《大语言模型》作者：赵鑫，李军毅，周昆，唐天一，文继荣

A curated list of reinforcement learning with human feedback resources (continually updated)

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorch

Minimal and Clean Reinforcement Learning Examples

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)