Advice-Based Exploration in Model-Based Reinforcement Learning

Canadian Conference on AI, pp. 72-83, 2018.

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Abstract:

Convergence to an optimal policy using model-based reinforcement learning can require significant exploration of the environment. In some settings such exploration is costly or even impossible, such as in cases where simulators are not available, or where there are prohibitively large state spaces. In this paper we examine the use of advi...More

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