Data-Driven Inverse Optimal Control for Continuous-Time Nonlinear Systems
This paper introduces a novel model-free and a partially model-free algorithm for inverse optimal control (IOC), also known as inverse reinforcement learning (IRL), aimed at estimating the cost function of continuous-time nonlinear deterministic systems. Using the input-state trajectories of an expert agent, the proposed algorithms separately utilize control policy information and the Hamilton-Jacobi-Bellman equation to estimate different sets of cost function parameters. This approach allows the algorithms to achieve broader applicability while maintaining a model-free framework. Also, the model-free algorithm reduces complexity compared to existing methods, as it requires solving a forward optimal control problem only once during initialization. Furthermore, in our partially model-free algorithm, this step can be bypassed entirely for systems with known input dynamics. Simulation results demonstrate the effectiveness and efficiency of our algorithms, highlighting their potential for real-world deployment in autonomous systems and robotics.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Multi-Mode Process Control Using Multi-Task Inverse Reinforcement Learning
In the era of Industry 4.0 and smart manufacturing, process systems engineering must adapt to digital transformation. While reinforcement learning offers a model-free approach to process control, its applications are lim…
Multi-Task Learningreinforcement-learningReinforcement LearningA Data-Driven Approach for Inverse Optimal Control
This paper proposes a data-driven, iterative approach for inverse optimal control (IOC), which aims to learn the objective function of a nonlinear optimal control system given its states and inputs. The approach solves t…
Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics
A fundamental question in neuroscience is how the brain creates an internal model of the world to guide actions using sequences of ambiguous sensory information. This is naturally formulated as a reinforcement learning p…
Deep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)eXplainable AI for data driven control: an inverse optimal control approach
Understanding the behavior of black-box data-driven controllers is a key challenge in modern control design. In this work, we propose an eXplainable AI (XAI) methodology based on Inverse Optimal Control (IOC) to obtain l…
Decision MakingEnergy-Based Continuous Inverse Optimal Control
The problem of continuous inverse optimal control (over finite time horizon) is to learn the unknown cost function over the sequence of continuous control variables from expert demonstrations. In this article, we study t…
Autonomous Drivingcontinuous-controlContinuous ControlTrajectory Prediction