Episode-based reinforcement learning-an instance-based approach for perceptual aliasing

Proposes a reinforcement learning method based on memorizing and retrieving episodes of the learner´s own experiences. The results of the computer simulation on a simple but typical non-Markovian environment is shown to clarify the performance. An instance-based reinforcement learning method previously proposed by Unemi (1992) is also based on the learner´s experiences memorized without any modification. But it is applicable only to the Markovian domain where it is enough for the learner to acquire a reactive policy to achieve the optimal behavior. An episode-based method not only overcomes perceptual aliasing but also inherits the advantages of the instance-based method on flexibility for applicable domains