An incremental sampling-based algorithm for stochastic optimal control
In this paper, we consider a class of continuous-time, continuous-space stochastic optimal control problems. Using the Markov chain approximation method and recent advances in sampling-based algorithms for deterministic path planning, we propose a novel algorithm called the incremental Markov Decisi...
| Main Authors: | , , |
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| Other Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publications,
2018-06-12T17:35:16Z.
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| Subjects: | |
| Online Access: | Get fulltext |
| Summary: | In this paper, we consider a class of continuous-time, continuous-space stochastic optimal control problems. Using the Markov chain approximation method and recent advances in sampling-based algorithms for deterministic path planning, we propose a novel algorithm called the incremental Markov Decision Process to incrementally compute control policies that approximate arbitrarily well an optimal policy in terms of the expected cost. The main idea behind the algorithm is to generate a sequence of finite discretizations of the original problem through random sampling of the state space. At each iteration, the discretized problem is a Markov Decision Process that serves as an incrementally refined model of the original problem. We show that with probability one, (i) the sequence of the optimal value functions for each of the discretized problems converges uniformly to the optimal value function of the original stochastic optimal control problem, and (ii) the original optimal value function can be computed efficiently in an incremental manner using asynchronous value iterations. Thus, the proposed algorithm provides an anytime approach to the computation of optimal control policies of the continuous problem. The effectiveness of the proposed approach is demonstrated on motion planning and control problems in cluttered environments in the presence of process noise. Keywords: Stochastic optimal control, dynamical systems, randomized methods, robotics National Science Foundation (U.S.) (Grant CNS-1016213) Arthur & Linda Gelb Tr Charitable Foundation |
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