Memory Approaches to Reinforcement Learning in Non-Markovian Domains
Long Lin, Tom M. Mitchell · 1992
Reinforcement learning is a type of unsupervised learning for sequential decision making. Qlearning is probably the best-understood reinforcement learning algorithm. In Q-learning, the agent learns a mapping from states and actions to their utilities. An important assumption of Q-learning is the Markovian environment assumption, meaning that any information needed to determine the optimal actions is reflected in the agent's state representation. Consider an agent whose state representation is based solely on its immediate perceptual sensations. When its sensors are not able to make essential distinctions among world states, the Markov assumption is violated, causing a problem called perceptual aliasing. For example, when facing a closed box, an agent based on its current visual sensation cannot act optimally if the optimal action depends on the contents of the box. There are two basic approaches to addressing this problem--- using more sensors or using history to figure out the curren...