Identifiability in inverse reinforcement learning
Inverse reinforcement learning attempts to reconstruct the reward function in a Markov decision problem, using observations of agent actions. As already observed in Russell [1998] the problem is ill-posed, and the reward function is not identifiable, even under the presence of perfect information ab...
| Main Authors: | , , |
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| Format: | Conference item |
| Language: | English |
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Neural Information Processing Systems Foundation
2021
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