Practice workspaces for Reinforcement Learning

Learning Goals Understand the Reinforcement Learning problem and how it differs from Supervised Learning

Summary Reinforcement Learning (RL) is concerned with goal-directed learning and decision-making. In RL an agent learns from experiences it gains by interacting with the environment. In Supervised Learning we cannot affect the environment. In RL rewards are often delayed in time and the agent tries to maximize a long-term goal. For example, one may need to make seemingly suboptimal moves to reach a winning position in a game. An agent interacts with the environment via states, actions and rewards. Lectures & Readings

Required: Reinforcement Learning: An Introduction http://incompleteideas.net/book/RLbook2018.pdf David Silver's RL Course https://gym.openai.com/docs