Reinforcement Learning

• At every step of interaction, the agent sees a (possibly partial) observation $o$ of the state $s$ of the world, and then decides on an action $a_t$ from the action space to take.
  • The agent also perceives a reward signal from the environment, a number that tells it how good or bad the current world state is.
  • The goal of the agent is to maximize its cumulative reward, called return.