Computing Complexity-aware Plans Using Kolmogorov Complexity
Abstract
In this paper, we introduce complexity-aware planning for finite-horizon deterministic finite automata with rewards as outputs, based on Kolmogorov complexity. Kolmogorov complexity is considered since it can detect computational regularities of deterministic optimal policies. We present a planning objective yielding an explicit trade-off between a policy's performance and complexity. It is proven that maximising this objective is non-trivial in the sense that dynamic programming is infeasible. We present two algorithms obtaining low-complexity policies, where the first algorithm obtains a low-complexity optimal policy, and the second algorithm finds a policy maximising performance while maintaining local (stage-wise) complexity constraints. We evaluate the algorithms on a simple navigation task for a mobile robot, where our algorithms yield low-complexity policies that concur with intuition.
- Publication:
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arXiv e-prints
- Pub Date:
- September 2021
- DOI:
- 10.48550/arXiv.2109.10303
- arXiv:
- arXiv:2109.10303
- Bibcode:
- 2021arXiv210910303S
- Keywords:
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- Electrical Engineering and Systems Science - Systems and Control;
- Computer Science - Artificial Intelligence;
- Mathematics - Optimization and Control
- E-Print:
- Accepted to CDC 2021