Game-theoretic inverse reinforcement learning: a differential pontryagin's maximum principle approach
This paper proposes a game-theoretic inverse reinforcement learning (GT-IRL) framework, which aims to learn the parameters in both the dynamic system and individual cost function of multistage games from demonstrated trajectories. Different from the probabilistic approaches in computer science commu...
| Váldodahkkit: | , |
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| Eará dahkkit: | |
| Materiálatiipa: | Journal Article |
| Giella: | English |
| Almmustuhtton: |
2022
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| Fáttát: | |
| Liŋkkat: | https://hdl.handle.net/10356/162585 |