pytorch-a2c-ppo-acktr-gail

Why we included this project

This repo is the reference implementation people point to when they start learning deep reinforcement learning. It covers the four policy-gradient algorithms most newcomers meet first, A2C, PPO, ACKTR, and GAIL, and each one is laid out plainly enough to read like a worked example rather than a framework you have to dig through. Tracing through the code is a good way to see how an advantage estimator slots into a policy update, how PPO keeps its updates from going too far, or how GAIL learns from demonstrations, all of which are easy to lose in the math. It also ships with tuned hyperparameters and Gym adapters for Atari, MuJoCo, PyBullet, and the DeepMind Control Suite, so you can run experiments quickly. One caveat: the author now steers people toward a newer JAX-based RL repository, so treat this as a learning and prototyping resource rather than something to drop into production.