Latent Learning in Agents
نویسنده
چکیده
Various cognitive models have been proposed to determine optimal paths in spatial navigation tasks, some of which demonstrate latent learning in Agents. We view and present the model as a Reinforcement Learning problem by using QLearning and Model-Based Learning in a deterministic environment. This paper uses a QLearning algorithm and compares its performance with the Dyna (Model based) algorithm on Blocking and Shortcut problems. We conclude that using Model-Based Reinforcement learning provides an interesting and efficient solution to blocking and shortcut problems and that the agent demonstrates Latent Learning.
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