May 11, 2020 · This paper tackles the challenge of using belief-based rewards for a deep RL agent, by offering a simple insight that maximizing any convex function of the ...
[PDF] Maximizing Information Gain in Partially Observable Environments ...
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ABSTRACT. Information gathering in a partially observable environment can be formulated as a reinforcement learning (RL), problem where the reward depends ...
May 13, 2020 · This insight provides theoretical motivation for several fields using prediction rewards---namely visual attention, question answering systems, ...
May 9, 2020 · This insight provides theoretical motivation for several fields using prediction rewards—namely visual attention, question answering systems, ...
This paper presents deep anticipatory networks (DANs), which enables an agent to take actions to reduce its uncertainty without performing explicit belief ...
Information gathering in a partially observable environment can be formulated as a reinforcement learning (RL), problem where the reward depends on the ...
This insight provides theoretical motivation for several fields using prediction rewards---namely visual attention, question answering systems, and intrinsic ...
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Bibliographic details on Maximizing Information Gain in Partially Observable Environments via Prediction Rewards.
Maximizing Information Gain in Partially Observable Environments via Prediction Rewards. Yash Satsangi, Sungsu Lim Lim, Shimon Whiteson, Frans A. Oliehoek ...