Efficient learning and planning within the Dyna framework
Adaptive Behavior
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Learning and discovery of predictive state representations in dynamical systems with reset
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning low dimensional predictive representations
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Observable Operator Models for Discrete Stochastic Time Series
Neural Computation
Unsupervised learning of image manifolds by semidefinite programming
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ICML '05 Proceedings of the 22nd international conference on Machine learning
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Learning action models for multi-agent planning
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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Planning involves using a model of an agent's actions to find a sequence of decisions which achieve a desired goal. It is usually assumed that the models are given, and such models often require expert knowledge of the domain. This paper explores subjective representations for planning that are learned directly from agent observations and actions (requiring no initial domain knowledge). A non-linear embedding technique called Action Respecting Embedding is used to construct such a representation. It is then shown how to extract the effects of the agent's actions as operators in this learned representation. Finally, the learned representation and operators are combined with search to find sequences of actions that achieve given goals. The efficacy of this technique is demonstrated in a challenging robot-vision-inspired image domain.