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.