Still , this may be a useful method in cases where it is known that only a small memory depth is needed. Both architectures use recurrent neural networks trained with gradient descent. Carnegie Mellon University, 1992. Carnegie Mellon University, 1992. Lin L-J. and Mitchell T.M., 1992.
Memory approaches to reinforcement learning in non-Markovian domains .
Learning to Balance a Pole. This creates a nonMarkovian state space. He considers both window based and recurrent evaluationfunction models, and finds that a window size of 1 works well. Usually, knowledgelean environments are accompanied by ill defined objectives and evaluative feedback, both of which preclude the use of the knowledge based model. In the hybrid model, the states and decisions are recorded in a dynamically managed history as decisions are made.