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
