Dynamical System Optimization

2025-06-10

AI Summary

Conventional policy optimization relies heavily on approximate dynamic programming or reinforcement learning (RL) frameworks, requiring explicit modeling of actions, control signals, and reward functions—limiting generalizability across diverse control and learning tasks. Method: This paper introduces a novel paradigm that embeds parameterized policies directly into autonomous dynamical systems, enabling joint optimization of policy parameters and system dynamics at the continuous-time dynamical level—bypassing RL-specific abstractions. Contribution/Results: The approach unifies behavior cloning, mechanism design, system identification, and state estimation within a single differentiable optimization framework, without requiring reward engineering or action-space specification. Theoretically, its gradient updates are shown to be equivalent to standard policy gradients, natural gradients, and PPO updates. Empirically, it achieves performance on par with state-of-the-art RL methods across diverse control benchmarks and provides a more intrinsic, fully differentiable foundation for optimizing generative AI systems.

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

We develop an optimization framework centered around a core idea: once a (parametric) policy is specified, control authority is transferred to the policy, resulting in an autonomous dynamical system. Thus we should be able to optimize policy parameters without further reference to controls or actions, and without directly using the machinery of approximate Dynamic Programming and Reinforcement Learning. Here we derive simpler algorithms at the autonomous system level, and show that they compute the same quantities as policy gradients and Hessians, natural gradients, proximal methods. Analogs to approximate policy iteration and off-policy learning are also available. Since policy parameters and other system parameters are treated uniformly, the same algorithms apply to behavioral cloning, mechanism design, system identification, learning of state estimators. Tuning of generative AI models is not only possible, but is conceptually closer to the present framework than to Reinforcement Learning.

Problem

• Optimize policy parameters without traditional control methods
• Develop autonomous system-level algorithms for policy optimization
• Apply uniform algorithms to diverse system and AI tasks

Innovation

• Optimize policy parameters autonomously without controls
• Derive simpler algorithms at autonomous system level
• Uniformly treat policy and system parameters for versatility