Inverse reinforcement learning with locally consistent reward functions
Existing inverse reinforcement learning (IRL) algorithms have assumed each expert’s demonstrated trajectory to be produced by only a single reward function. This paper presents a novel generalization of the IRL problem that allows each trajectory to be generated by multiple locally consistent reward...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Neural Information Processing Systems Foundation
2018
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| Online Access: | http://hdl.handle.net/1721.1/113094 https://orcid.org/0000-0002-8585-6566 |