Clustering Markov Decision Processes For Continual Transfer

15 Nov 2013M. M. Hassan MahmudMajd HawaslyBenjamin RosmanSubramanian Ramamoorthy

We present algorithms to effectively represent a set of Markov decision processes (MDPs), whose optimal policies have already been learned, by a smaller source subset for lifelong, policy-reuse-based transfer learning in reinforcement learning. This is necessary when the number of previous tasks is large and the cost of measuring similarity counteracts the benefit of transfer... (read more)

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