DemPref uses demonstrations to form a coarse reward prior and ground active preference queries, achieving higher efficiency than pure preference learning and higher user preference than IRL in experiments.
Continuous Inverse Optimal Control with Locally Optimal Examples
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abstract
Inverse optimal control, also known as inverse reinforcement learning, is the problem of recovering an unknown reward function in a Markov decision process from expert demonstrations of the optimal policy. We introduce a probabilistic inverse optimal control algorithm that scales gracefully with task dimensionality, and is suitable for large, continuous domains where even computing a full policy is impractical. By using a local approximation of the reward function, our method can also drop the assumption that the demonstrations are globally optimal, requiring only local optimality. This allows it to learn from examples that are unsuitable for prior methods.
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cs.RO 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Learning Reward Functions by Integrating Human Demonstrations and Preferences
DemPref uses demonstrations to form a coarse reward prior and ground active preference queries, achieving higher efficiency than pure preference learning and higher user preference than IRL in experiments.