Define the RL setting: agent, environment, state, action, reward, policy.
easyAnswer
- An agent observes a state s from the environment, picks an action a via its policy , receives a reward r and a new state s'.
- The goal is to maximize the expected cumulative discounted reward (return).
- Formalized as a Markov Decision Process (S, A, P, R, gamma).
- The agent learns pi (or a value function) from interaction with the environment.
Check yourself — multiple choice
- The policy maps rewards to states
- The policy maps states to a distribution over actions
- The policy is the same as the value function
- RL requires labeled examples
A policy is — a distribution over actions given a state.
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Practise Reinforcement Learning
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