Reinforcement Learning in Markovian and Non-Markovian Environments
نویسنده
چکیده
This work addresses three problems with reinforcement learning and adap-tive neuro-control: 1. Non-Markovian interfaces between learner and environment. 2. On-line learning based on system realization. 3. Vector-valued adaptive critics. An algorithm is described which is based on system realization and on two interacting fully recurrent continually running networks which may learn in parallel. Problems with parallel learning are attacked byàdaptive randomness'. It is also described how interacting model/controller systems can be combined with vector-valuedàdaptive critics' (previous critics have been scalar).
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