Part 1: Key Concepts in RL — Spinning Up documentation

In a nutshell, RL is the study of agents and how they learn by trial and error. It formalizes the idea that rewarding or punishing an agent for its behavior makes it more likely to repeat or forego that behavior in the future. RL methods have recently enjoyed a wide variety of successes. For example, it’s been used to teach computers to control robots in simulation... ...and in the real world... It’s also famously been used to create breakthrough AIs for sophisticated strategy games, most notably Go and Dota, taught computers to play Atari games from raw pixels, and trained simulated robots to follow human instructions. Agent-environment interaction loop. The main characters of RL are the agent and the environment. The environment is the world that the agent lives in and interacts with. At every step of interaction, the agent sees a (possibly partial) observation of the state of the world, and then decides on an action to take. The environment changes when the agent acts on it, but may

Part 1: Key Concepts in RL - Spinning Up documentation --> Docs >> Part 1: Key Concepts in RL Edit on GitHub Part 1: Key Concepts in RL ¶ Table of Contents Part 1: Key Concepts in RL What Can RL Do? Key Concepts and Terminology (Optional) Formalism Welcome to our introduction to reinforcement learning! Here, we aim to acquaint you with the language and notation used to discuss the subject, a high-level explanation of what RL algorithms do (although we mostly avoid the question of how they do it), and a little bit of the core math that underlies the algorithms. In a nutshell, RL is the study of

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