The complexity of Markov decision processes
Mathematics of Operations Research
A model for reasoning about persistence and causation
Computational Intelligence
Abstraction and approximate decision-theoretic planning
Artificial Intelligence
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Complexity of finite-horizon Markov decision process problems
Journal of the ACM (JACM)
Monotonic reductions, representative equivalence, and compilation of intractable problems
Journal of the ACM (JACM)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computing Factored Value Functions for Policies in Structured MDPs
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Preprocessing of intractable problems
Information and Computation
The size of MDP factored policies
Eighteenth national conference on Artificial intelligence
Some connections between nonuniform and uniform complexity classes
STOC '80 Proceedings of the twelfth annual ACM symposium on Theory of computing
Dynamic Programming
Speeding up the convergence of value iteration in partially observable Markov decision processes
Journal of Artificial Intelligence Research
The comparative linguistics of knowledge representation
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Probabilistic propositional planning: representations and complexity
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
On the complexity of solving Markov decision problems
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
The size of MDP factored policies
Eighteenth national conference on Artificial intelligence
Factored value iteration converges
Acta Cybernetica
Factored temporal difference learning in the new ties environment
Acta Cybernetica
Optimistic initialization and greediness lead to polynomial time learning in factored MDPs
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
On polynomial sized MDP succinct policies
Journal of Artificial Intelligence Research
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Policies of Markov Decision Processes (MDPs) tell the next action to execute, given the current state and (possibly) the history of actions executed so far. Factorization is used when the number of states is exponentially large: both the MDP and the policy can be then represented using a compact form, for example employing circuits. We prove that there are MDPs whose optimal policies require exponential space evenin factored form.