Agent in the Reinforcement Learning framework. The agent interacts with the environment by selecting actions given a state. The rule for this selection is based on a policy. The agents goal is to find the policy that maximizes the expected cumulative sum of rewards. We distinguish problems with discrete and continuous action spaces. Sub-Categories: