One often investigated problem results from dealing with a large state space. L.-J. Lin and T. Mitchell. Memory approaches to reinforcement learning in non-markovian domains . 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 .
One way to include memory in a reinforcement learning algorithm like Q learning is to use a recurrent neural network to represent the Qvalues. This approach works well on small problems but may suffer from local optima in complex problems. Memory approaches to reinforcement learning in non-Markovian domains . One way to include memory in a reinforcement learning algorithm like Q learning is to use a recurrent neural network to represent the Q values. Memory approaches to reinforcement learning in non-Markovian domains .