Reinforcement Learning in Markovian and Non-Markovian Environments

This work addresses three problems with reinforcement learning and adap(cid:173) tive neuro-control: 1. Non-Markovian interfaces between learner and en(cid:173) vironment. An algorithm is described which is based on system realization and on two interacting fully recurrent continually running net(cid:173) works which may learn in parallel. Problems with parallel learning are attacked by'adaptive randomness'. It is also described how interacting model/controller systems can be combined with vector-valued'adaptive critics' (previous critics have been scalar).