We introduce the notion of LImited Memory Influence Diagram (LIMID) to describe multistage decision problems in which the traditional assumption of no forgetting is relaxed. This can be relevant in situations with multiple decision makers or when decisions must be prescribed under memory constraints, such as in partially observed Markov decision processes (POMDPs). We give an algorithm for improving any given strategy by local computation of single policy updates and investigate conditions for the resulting strategy to be optimal.Local Computation, Message Passing, Optimal Strategies, Partially Observed Markov Decision Process, Single Policy Updating
Partially observable Markov decision processes (POMDPs) provide a natural and principled framework t...
Policy-gradient algorithms are attractive as a scalable approach to learning approximate policies fo...
Influence diagrams are intuitive and concise representations of structured decision problems. When t...
\u3cp\u3eWe present a new algorithm for exactly solving decision making problems represented as inue...
LImited Memory Influence Diagrams (LIMIDs) are general models of decision problems for representing ...
A limited-memory influence diagram (LIMID) is a gen-eralization of a traditional influence diagram i...
We present a new algorithm for exactly solving decision-making problems rep-resented as an influence...
47 pages, 3 figuresThis paper introduces algorithms for problems where a decision maker has to contr...
Partially observable decision processes (POMDP) can be used as a model for planning in stochastic do...
Partially observable Markov decision process (POMDP) can be used as a model for planning in stochast...
As agents are built for ever more complex environments, methods that consider the uncertainty in the...
The concept of partially observable Markov decision processes was born to handle the problem of lack...
: Partially-observable Markov decision processes provide a very general model for decision-theoretic...
A very general framework for modeling uncertainty in learning environments is given by Partially Obs...
Partially observable Markov decision process (POMDP) is a formal model for planning in stochastic do...
Partially observable Markov decision processes (POMDPs) provide a natural and principled framework t...
Policy-gradient algorithms are attractive as a scalable approach to learning approximate policies fo...
Influence diagrams are intuitive and concise representations of structured decision problems. When t...
\u3cp\u3eWe present a new algorithm for exactly solving decision making problems represented as inue...
LImited Memory Influence Diagrams (LIMIDs) are general models of decision problems for representing ...
A limited-memory influence diagram (LIMID) is a gen-eralization of a traditional influence diagram i...
We present a new algorithm for exactly solving decision-making problems rep-resented as an influence...
47 pages, 3 figuresThis paper introduces algorithms for problems where a decision maker has to contr...
Partially observable decision processes (POMDP) can be used as a model for planning in stochastic do...
Partially observable Markov decision process (POMDP) can be used as a model for planning in stochast...
As agents are built for ever more complex environments, methods that consider the uncertainty in the...
The concept of partially observable Markov decision processes was born to handle the problem of lack...
: Partially-observable Markov decision processes provide a very general model for decision-theoretic...
A very general framework for modeling uncertainty in learning environments is given by Partially Obs...
Partially observable Markov decision process (POMDP) is a formal model for planning in stochastic do...
Partially observable Markov decision processes (POMDPs) provide a natural and principled framework t...
Policy-gradient algorithms are attractive as a scalable approach to learning approximate policies fo...
Influence diagrams are intuitive and concise representations of structured decision problems. When t...