basic_reinforcement_learning

Why we included this project

Dense papers make reinforcement learning harder to get into than it needs to be. This series works the other way: each classic algorithm comes with working code and a step-by-step walkthrough, starting with tabular methods like Q-learning and SARSA and moving up to DQN, convolutional Q-learning, and DDPG. Because every tutorial is self-contained and built on OpenAI Gym, you can run, modify, and compare the algorithms yourself instead of just reading about them. One chapter stands out: it applies RL to ROS and Gazebo, which is useful if you are heading toward robotics. The whole thing is organized like a course rather than scattered snippets, so it works well for developers who learn by running code.