A neural reinforcement learning model for tasks with unknown time delays

35th Annual Conference of the Cognitive Science Society, 2013

Daniel Rasmussen, Chris Eliasmith


We present a biologically based neural model capable of performing reinforcement learning in complex tasks. The model is unique in its ability to solve tasks that require the agent to make a sequence of unrewarded actions in order to reach the goal, in an environment where there are unknown and variable time delays between actions, state transitions, and rewards. Specifically, this is the first neural model of reinforcement learning able to function within a Semi-Markov Decision Process (SMDP) framework. We believe that this extension of current modelling efforts lays the groundwork for increasingly sophisticated models of human decision making.

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35th Annual Conference of the Cognitive Science Society


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