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Agent-Aware State Estimation in Autonomous Vehicles
We introduce agent-aware state estimation—a framework for calculating indirect estimations of state given observations of the behavior of other agents in the environment.
Shane Parr
,
Ishan Khatri
,
Justin Svegliato
,
Shlomo Zilberstein
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Planning with Abstract Learned Models While Learning Transferable Subtasks
We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple levels of abstraction.
John Winder
,
Stephanie Milani
,
Matthew Landen
,
Erebus Oh
,
Shane Parr
,
Shawn Squire
,
Marie desJardins
,
Cynthia Matuszek
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