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Adaptive Interest for Emphatic Reinforcement Learning
Martin Klissarov · Rasool Fakoor · Jonas Mueller · Kavosh Asadi · Taesup Kim · Alexander Smola

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #517

Emphatic algorithms have shown great promise in stabilizing and improving reinforcement learning by selectively emphasizing the update rule. Although the emphasis fundamentally depends on an interest function which defines the intrinsic importance of each state, most approaches simply adopt a uniform interest over all states (except where a hand-designed interest is possible based on domain knowledge). In this paper, we investigate adaptive methods that allow the interest function to dynamically vary over states and iterations. In particular, we leverage meta-gradients to automatically discover online an interest function that would accelerate the agent’s learning process. Empirical evaluations on a wide range of environments show that adapting the interest is key to provide significant gains. Qualitative analysis indicates that the learned interest function emphasizes states of particular importance, such as bottlenecks, which can be especially useful in a transfer learning setting.

Author Information

Martin Klissarov (Mila/McGill University)
Rasool Fakoor (Amazon Web Services)
Jonas Mueller (Amazon Web Services)
Kavosh Asadi (Amazon)
Taesup Kim (Seoul National University)
Alexander Smola (Amazon)

**AWS Machine Learning**

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