Maximizing Information Gain in Partially Observable Environments via Prediction Rewards
Information gathering in a partially observable environment can be formulated as a reinforcement learning (RL), problem where the reward depends on the agent’s uncertainty. For example, the reward can be the negative entropy of the agent’s belief over an unknown (or hidden) variable. Typically, the...
| Main Authors: | , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
2020
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