General purpose intelligent learning agents cycle through
(complex,non-MDP) sequences of observations, actions, and
rewards. On the other hand, reinforcement learning is welldeveloped
for small finite state Markov Decision Processes
(MDPs). So far it is an art performed by human designers to
extract the right state representation out of the bare observations,
i.e. to reduce the agent setup to the MDP framework.
Before we can think of mechanizing this search for suitable
MDPs, we need a...[Show more]
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.