Approximate Planning for Factored POMDPs using Belief State Simplification
نویسندگان
چکیده
We are interested in the problem of plan ning for factored POMOPs. Building on the recent results of Kearns, Mansour and Ng, we provide a planning algorithm for fac tored POMOPs that exploits the accuracy efficiency tradeoff in the belief state sim plifi cation introduced by Boyen and Koller.
منابع مشابه
Approximate Planning for Factored POMDPs using Belief State Simpli cation
We are interested in the problem of planning for factored POMDPs. Building on the recent results of Kearns, Mansour and Ng, we provide a planning algorithm for fac-tored POMDPs that exploits the accuracy-eeciency tradeoo in the belief state simplii-cation introduced by Boyen and Koller.
متن کاملReinforcement Learning Using Approximate Belief States
Ronald Parr, Daphne Koller Computer Science Department Stanford University Stanford, CA 94305 {parr,koller}@cs.stanford.edu The problem of developing good policies for partially observable Markov decision problems (POMDPs) remains one of the most challenging areas of research in stochastic planning. One line of research in this area involves the use of reinforcement learning with belief states,...
متن کاملPOMDPs for robotic tasks with mixed observability
Partially observable Markov decision processes (POMDPs) provide a principled mathematical framework for motion planning of autonomous robots in uncertain and dynamic environments. They have been successfully applied to various robotic tasks, but a major challenge is to scale up POMDP algorithms for more complex robotic systems. Robotic systems often have mixed observability: even when a robot’s...
متن کاملAn Approach to State Aggregation for POMDPs
A partially observable Markov decision process (POMDP) provides an elegant model for problems of planning under uncertainty. Solving POMDPs is very computationally challenging, however, and improving the scalability of POMDP algorithms is an important research problem. One way to reduce the computational complexity of planning using POMDPs is by using state aggregation to reduce the (effective)...
متن کاملSolving Factored POMDPs with Linear Value Functions
Partially Observable Markov Decision Processes (POMDPs) provide a coherent mathematical framework for planning under uncertainty when the state of the system cannot be fully observed. However, the problem of finding an exact POMDP solution is intractable. Computing such solution requires the manipulation of a piecewise linear convex value function, which specifies a value for each possible beli...
متن کامل