An important future research question is whether similar methods can resolve all of the hidden states in a large, real world application. Memory approaches to reinforcement learning in nonmarkovian domains . Carnegie Mellon University, School of Computer Science. The desired quantity at each step of a backgammon game is the continuous valued probability of winning, whereas the desired output of a phoneme recognition problem is a classification decision among 50 or more discrete choices. L.-J. Lin and T. Mitchell.
Memory approaches to reinforcement learning in nonMarkovian domains .
It seems to work effectively on simple problems, but can suffer from convergence to local optima on more complex problems. Memory approaches to reinforcement learning in nonMarkovian domains . Other works in reinforcement learning deal with the perceptual aliasing problem by extending the TD methods. These methods cannot help in the mentioned situation for the task of finding a block. Memory cannot help because we can face this situation with independence of the past actions taken.