🤖 The Little Book of Reinforcement Learning is a short introduction to reinforcement learning, from basic concepts to applied algorithms.
The repository complements the book with PyTorch implementations of the covered algorithms, from Monte Carlo methods to PPO. It also includes supplementary material with detailed explanations and rigorous proofs of dynamic programming algorithms.
In this paradigm, an agent interacts with an environment, observes states, chooses actions, and receives rewards. Over time it learns a policy that maximizes cumulative reward while balancing exploration of new options with exploiting what already works.
📘 The project is useful for connecting theory with executable code. The book is distributed under a non-commercial Creative Commons BY-SA 4.0 license.
💡 Explanation in a nutshell#
Reinforcement learning is like training a player through rewards and penalties. The agent tries actions, observes their consequences, and adjusts its strategy. The reward does not say exactly what to do: it indicates how good the decision was and forces the system to learn through experience.
More information at the link 👇
