Bisimulation through probabilistic testing
Information and Computation
Online minimization of transition systems (extended abstract)
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
Using abstractions for decision-theoretic planning with time constraints
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Formal Methods in System Design - Special issue on symmetry in automatic verification
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Bounded-parameter Markov decision process
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Heuristic Approach to the Discovery of Macro-Operators
Machine Learning
Symmetry in Markov Decision Processes and its Implications for Single Agent and Multiagent Learning
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Equivalence notions and model minimization in Markov decision processes
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Symmetries and Model Minimization in Markov Decision Processes
Symmetries and Model Minimization in Markov Decision Processes
Temporal abstraction in reinforcement learning
Temporal abstraction in reinforcement learning
Algebraic structure theory of sequential machines (Prentice-Hall international series in applied mathematics)
Exploiting structure in policy construction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Model minimization in Markov decision processes
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Spatiotemporal Abstraction of Stochastic Sequential Processes
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Using Homomorphisms to transfer options across continuous reinforcement learning domains
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Abstraction in predictive state representations
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
State similarity based approach for improving performance in RL
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Efficiently exploiting symmetries in real time dynamic programming
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Efficiently exploiting symmetries in real time dynamic programming
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
SMDP homomorphisms: an algebraic approach to abstraction in semi-Markov decision processes
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Learning to generalize and reuse skills using approximate partial policy homomorphisms
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
On reduction criteria for probabilistic reward models
FSTTCS'06 Proceedings of the 26th international conference on Foundations of Software Technology and Theoretical Computer Science
Structural abstraction experiments in reinforcement learning
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Abstraction and generalization in reinforcement learning: a summary and framework
ALA'09 Proceedings of the Second international conference on Adaptive and Learning Agents
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When applied to real world problems Markov Decision Processes (MDPs) often exhibit considerable implicit redundancy, especially when there are symmetries in the problem. In this article we present an MDP minimization framework based on homomorphisms. The framework exploits redundancy and symmetry to derive smaller equivalent models of the problem. We then apply our minimization ideas to the options framework to derive relativized options--options defined without an absolute frame of reference. We demonstrate their utility empirically even in cases where the minimization criteria are not met exactly.