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аналитика - Inverse Reinforcement Learning
Learning Reward Functions and Discount Factors from Experts with Multiple Planning Horizons
We develop algorithms to jointly learn a global reward function and agent-specific discount factors from expert demonstrations with different planning horizons.
A Bayesian Approach to Robust Inverse Reinforcement Learning for High-Dimensional Continuous Control
The core message of this paper is that a Bayesian approach to model-based inverse reinforcement learning (BM-IRL) can lead to robust policies by simultaneously estimating the expert's reward function and their internal model of the environment dynamics. This is achieved by incorporating a prior that encodes the accuracy of the expert's dynamics model, which encourages the learner to plan against the worst-case dynamics outside the offline data distribution.
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