Reinforcement Learning in Markovian and Non-Markovian Environments

Part of Advances in Neural Information Processing Systems 3 (NIPS 1990)

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Jürgen Schmidhuber


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. 2. On-line learning based on system realization. 3. Vector(cid:173) valued adaptive critics. 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).