Memory approaches to reinforcement learning in nonMarkovian domains . Memory approaches to reinforcement learning in nonMarkovian domains . All other methods use some sort of memory. 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. 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 .
However, even in a Markovian task, the single job of learning the direct mapping can be difficult. Only when the task is non Markovian is it necessary to equip the agent with some sort of internal state. However, even in a Markovian task, the single job of learning the direct mapping can be difficult. One often investigated problem results from dealing with a large state space. L.-J. Lin and T. Mitchell.