Abstract
Mutual learning is an emerging technique for allowing intelligent systems to learn from each other, giving rise to improved performance. In this paper, we explore mutual reinforcement learning between systems which use very different learning algorithms. In particular, we present an algorithm which allows two agents, one using Q-learning and another using adaptive dynamic programming, to share learned knowledge. We discuss how these agents negotiate the relative importance of knowledge they receive from other agents, and we present results that show how this affects the learning process.
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