Deterministic Policy Gradient for Learning Equilibrium in Time-Inconsistent Control Problems
arXiv:2606.11798v1 Announce Type: cross Abstract: In this paper, we develop a continuous-time model-free reinforcement learning algorithm to learn deterministic equilibrium policies in general time-inconsistent control problems. Utilizing the extended Hamilton-Jacobi-Bellman system, we recast the original time-inconsistent problem into an equivalent two-stage problem. In the first stage, for given auxiliary functions, we employ the deterministic policy gradient approach to learn an optimal policy in an auxiliary time-consistent control problem. In the second stage, given the updated policy, we
The continuous-time model-free reinforcement learning approach combines recent advances in AI with the need for robust control mechanisms in complex, time-inconsistent systems.
This research provides a foundational step towards more sophisticated and autonomous AI systems capable of handling dynamic, real-world problems with inherent time-dependencies and evolving objectives.
The development of deterministic policy gradients allows for more stable and predictable learning in scenarios where optimal policies are time-varying and influenced by future decisions, which enhances the reliability of autonomous systems.
- · AI/ML researchers
- · Autonomous system developers
- · Financial modeling institutions
- · Robotics
- · Traditional control system designers (without ML integration)
- · Systems highly sensitive to time-inconsistency without adaptive controls
Improved performance and adaptability of AI agents in complex environments with long-term planning horizons.
Accelerated development of AI systems for critical infrastructures and financial markets requiring robust, self-learning equilibrium policies.
Potential for new economic models and automated decision-making frameworks that can optimally navigate time-inconsistent preferences at scale.
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