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Autonomous Exploration in Reinforcement Learning

Autonomous Exploration in Reinforcement Learning

This video was recorded at NIPS Workshops, Sierra Nevada 2011. One of the striking differences between current reinforcement learning algorithms and early human learning is that animals and infants appear to explore their environments with autonomous purpose, in a manner appropriate to their current level of skills. For analysing such autonomous exploration theoretically, an evaluation criterion is required to compare exploration algorithms. Unfortunately, no commonly agreed evaluation criterion has been established yet. As one possible criterion, we consider in this work the navigation skill of a learning agent after a number of exploration steps. In particular, we consider how many exploration steps are required, until the agent has learned reliable policies for reaching all states in a... Show More
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