Non-Markovian Control with Gated End-to-End Memory Policy Networks

31 May 2017 Julien Perez Tomi Silander

Partially observable environments present an important open challenge in the domain of sequential control learning with delayed rewards. Despite numerous attempts during the two last decades, the majority of reinforcement learning algorithms and associated approximate models, applied to this context, still assume Markovian state transitions... (read more)

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Memory Network
Working Memory Models