Elements of Reinforcement Learning

Elements of reinforcement learning:

• a policy: is a mapping from perceived states of the environment to actions to be taken when in those states.
• a reward function: defines the goal in a reinforcement learning problem. iIt maps each perceived state (or state-action pair) of the environment to a single number, a reward, indicating the intrinsic desirability of that state.
• a value function: specifies what is good in the long run. The value of a state is the total amount of reward an agent can expect to accumulate over the future, starting from that state.
• a model (optional)

reference: Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto