Reinforcement-Learning

Why we included this project

For developers who already know supervised learning and are curious how agents learn from interaction, this is one of the clearer on-ramps around. It is organized as a 60-day study plan: lectures pulled from DeepMind and Berkeley sit alongside PyTorch notebooks that walk through the classic algorithms, from Q-learning and DQN to actor-critic methods like A2C and PPO. Since most topics pair the theory with a runnable example, many of them exercised against OpenAI Gym environments such as Atari and RoboSchool, you can read about an approach and immediately watch it work. Treat it as a course companion rather than production code. If you already have the basics, the later weeks on model-based RL, imitation learning, and evolution strategies round out the picture.