Associative Reinforcement Learning

Synonyms

Associative bandit problem; Bandit problem with side information; Bandit problem with side observations; One-step reinforcement learning

Definition

The associative reinforcement-learning problem is a specific instance of the reinforcement learning problem whose solution requires generalization and exploration but not temporal credit assignment. In associative reinforcement learning, an action (also called an arm) must be chosen from a fixed set of actions during successive timesteps and from this choice a real-valued reward or payoff results. On each timestep, an input vector is provided that along with the action determines, often probabilistically, the reward. The goal is to maximize the expected long-term reward over a finite or infinite horizon. It is typically assumed that the action choices do not affect the sequence of input vectors. However, even if this assumption is not asserted, learning algorithms are not required to infer or model the relationship between input...