Learning to Perceive and Act by Trial and Error
Machine Learning
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
A reinforcement learning model of selective visual attention
Proceedings of the fifth international conference on Autonomous agents
Equivalence notions and model minimization in Markov decision processes
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
The Dynamic Structure of Everyday Life
The Dynamic Structure of Everyday Life
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
SMDP homomorphisms: an algebraic approach to abstraction in semi-Markov decision processes
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The thing that we tried didn't work very well: deictic representation in reinforcement learning
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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Deictic representation is a representational paradigm, based on selective attention and pointers, that allows an agent to learn and reason about rich complex environments. In this article we present a hierarchical reinforcement learning framework that employs aspects of deictic representation. We also present a Bayesian algorithm for learning the correct representation for a given sub-problem and empirically validate it on a complex game environment.