Reinforcement learning can be formalized in terms of ____ in which the agent…

Correct answer: Markov decision processes, states

Explanation: Reinforcement learning is typically formalized as a Markov decision process (MDP). An MDP defines the environment for an agent using the following components:

• States (S): the set of possible states the agent can be in.

• Actions (A): the set of actions the agent can take.

• Transition function (P): probabilities of moving between states given actions.

• Reward function (R): rewards received after transitions.

At the start the agent is assumed to know the state space and the action space but must learn which actions yield high cumulative reward in each state.

Why the other choices are incorrect:

• The phrase using "objects" in place of states is incorrect because the formal term for the environment description is the set of states, not objects.

• "Hidden states" refers to partially observable settings (handled by POMDPs), which is a different formalism; it also does not address the incorrect use of the term "objects."

• In summary, the standard and concise formalization is a Markov decision process where the agent knows the set of possible states and the set of possible actions.