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Synapse: Learning Preferential Concepts from Visual Demonstrations
This paper addresses the problem of preference learning, which aims to learn user-specific preferences (e. g., "good parking spot", "convenient drop-off location") from visual input.
RAPTOR: End-to-end Risk-Aware MDP Planning and Policy Learning by Backpropagation
The direct optimization of this empirical objective in an end-to-end manner is called the risk-averse straight-line plan, which commits to a sequence of actions in advance and can be sub-optimal in highly stochastic domains.
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