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A Novel Variational Lower Bound for Inverse Reinforcement Learning
In this paper, we present a new Variational Lower Bound for IRL (VLB-IRL), which is derived under the framework of a probabilistic graphical model with an optimality node.
IRL with Partial Observations using the Principle of Uncertain Maximum Entropy
We show that in generalizing the principle of maximum entropy to these types of scenarios we unavoidably introduce a dependency on the learned model to the empirical feature expectations.
Adaptive Agent Architecture for Real-time Human-Agent Teaming
Previous human-human team research have shown complementary policies in TSF game and diversity in human players' skill, which encourages us to relax the assumptions on human policy.
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