Potential-based Shaping in Model-based Reinforcement Learning

Potential-based shaping was designed as a way of introducing background knowledge into model-free reinforcement-learning algorithms. By identifying states that are likely to have high value, this approach can decrease experience complexity—the number of trials needed to find near-optimal behavior. An orthogonal way of decreasing experience complexity is to use a model-based learning approach, building and exploiting an explicit transition model. In this paper, we show how potential-based shaping can be redefined to work in the model-based setting to produce an algorithm that shares the benefits of both ideas.

Authors

• John Asmuth Michael L. Littman

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