A past policy provides a bias to guide the exploration of the environment and speed up the learning of a new action policy. The success of this bias depends on whether the past policy is similar to the actual policy or not. In this report we describe a new algorithm, PRQ-Learning, that reuses a set of past policies to bias the learning of a new one. The past policies are ranked following a similarity metric that estimates how useful is to reuse each of those past policies. This ranking provides a probabilistic bias for the exploration in the new learning process. Several experiments demonstrate that PRQ-Learning finds a balance between exploitation of the ongoing learned policy, exploration of random actions, and exploration toward the past policies.

15 pages

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