Mutual Information Optimal Control of Discrete-Time Linear Systems
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Abstract (arXiv)
In this paper, we formulate a mutual information optimal control problem (MIOCP) for discrete-time linear systems. This problem can be regarded as an extension of a maximum entropy optimal control problem (MEOCP). Differently from the MEOCP where the prior is fixed to the uniform distribution, the MIOCP optimizes the policy and prior simultaneously. As analytical results, under the policy and prior classes consisting of Gaussian distributions, we derive the optimal policy and prior of the MIOCP with the prior and policy fixed, respectively. Using the results, we propose an alternating minimization algorithm for the MIOCP. Through numerical experiments, we discuss how our proposed algorithm works.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 7 Jul 2025.
Builds on
9 of 15 references.
References · 9
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
- 2017Reinforcement Learning with Deep Energy-Based Policiescited by 669
- 2021Maximum Entropy RL (Provably) Solves Some Robust RL Problemscited by 176
- 2018A Tour of Reinforcement Learning: The View from Continuous Controlcited by 151
- 2019Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learningcited by 18
- 2015LQG Control with Minimum Directed Information: Semidefinite Programming Approachcited by 8
- 2022Maximum entropy optimal density control of discrete-time linear systems and Schr"odinger bridgescited by 6
- 2023Maximum Entropy Density Control of Discrete-Time Linear Systems with Quadratic Costcited by 6
- 2021Maximum Entropy Differential Dynamic Programmingcited by 5
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.