Deep-reinforcement-learning-with-pytorch

Why we included this project

Getting from theory to working code in deep reinforcement learning usually means juggling a dozen different implementations. This repository shortens that step. It covers the classic algorithms, DQN, policy gradient, actor-critic, A2C/A3C, DDPG, TRPO, PPO, SAC, and TD3, each as a self-contained PyTorch script you can run against standard Gym environments like CartPole, MountainCar, Pendulum, and BipedalWalker. The author's stated goal is clear, readable code for learning, and the structure follows that: each chapter pairs an implementation with the original paper and practical notes, such as why a sparse-reward task like MountainCar behaves the way it does. That makes it a useful study aid for anyone who wants to compare how these algorithms are actually coded rather than just read about them. It is not a maintained production library, so treat it as a learning reference and a starting point for your own experiments rather than something to drop straight into a service.