Probabilistic inverse optimal control with local linearization for non-linear partially observable systems
| Autor: | Straub, Dominik, Schultheis, Matthias, Koeppl, Heinz, Rothkopf, Constantin A. |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Machine Learning (stat.ML) Systems and Control (eess.SY) Electrical Engineering and Systems Science - Systems and Control Machine Learning (cs.LG) Optimization and Control (math.OC) Statistics - Machine Learning FOS: Biological sciences Quantitative Biology - Neurons and Cognition FOS: Electrical engineering electronic engineering information engineering FOS: Mathematics Neurons and Cognition (q-bio.NC) Mathematics - Optimization and Control |
| Popis: | Inverse optimal control methods can be used to characterize behavior in sequential decision-making tasks. Most existing work, however, requires the control signals to be known, or is limited to fully-observable or linear systems. This paper introduces a probabilistic approach to inverse optimal control for stochastic non-linear systems with missing control signals and partial observability that unifies existing approaches. By using an explicit model of the noise characteristics of the sensory and control systems of the agent in conjunction with local linearization techniques, we derive an approximate likelihood for the model parameters, which can be computed within a single forward pass. We evaluate our proposed method on stochastic and partially observable version of classic control tasks, a navigation task, and a manual reaching task. The proposed method has broad applicability, ranging from imitation learning to sensorimotor neuroscience. |
| Databáze: | OpenAIRE |
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