Reinforcement Learning with History Lists
A very general framework for modeling uncertainty in learning environments is given by Partially observable Markov Decision Processes (POMDPs). In a POMDP setting, the learning agent infers a policy for acting optimally in all possible states of the environment, while receiving only observations of these states. The basic idea for coping with partial observability is to include memory into the ...