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On Convex Data-Driven Inverse Optimal Control for Nonlinear, Non-stationary and Stochastic Systems
This paper is concerned with a finite-horizon inverse control problem, which has the goal of inferring, from observations, the possibly non-convex and non-stationary cost driving the actions of an agent.
A Model-Based Reinforcement Learning Approach for PID Design
Proportional-integral-derivative (PID) controller is widely used across various industrial process control applications because of its straightforward implementation.
Model-based Reinforcement Learning
reinforcement-learning
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