Efficient Stochastic Optimal Control through Approximate Bayesian Input Inference
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866912086061219840 |
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| author | Watson, Joe Abdulsamad, Hany Findeisen, Rolf Peters, Jan |
| author_facet | Watson, Joe Abdulsamad, Hany Findeisen, Rolf Peters, Jan |
| contents | Optimal control under uncertainty is a prevailing challenge for many reasons. One of the critical difficulties lies in producing tractable solutions for the underlying stochastic optimization problem. We show how advanced approximate inference techniques can be used to handle the statistical approximations principled and practically by framing the control problem as a problem of input estimation. Analyzing the Gaussian setting, we present an inference-based solver that is effective in stochastic and deterministic settings and was found to be superior to popular baselines on nonlinear simulated tasks. We draw connections that relate this inference formulation to previous approaches for stochastic optimal control and outline several advantages that this inference view brings due to its statistical nature. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2105_07693 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Efficient Stochastic Optimal Control through Approximate Bayesian Input Inference Watson, Joe Abdulsamad, Hany Findeisen, Rolf Peters, Jan Machine Learning Robotics Systems and Control Optimal control under uncertainty is a prevailing challenge for many reasons. One of the critical difficulties lies in producing tractable solutions for the underlying stochastic optimization problem. We show how advanced approximate inference techniques can be used to handle the statistical approximations principled and practically by framing the control problem as a problem of input estimation. Analyzing the Gaussian setting, we present an inference-based solver that is effective in stochastic and deterministic settings and was found to be superior to popular baselines on nonlinear simulated tasks. We draw connections that relate this inference formulation to previous approaches for stochastic optimal control and outline several advantages that this inference view brings due to its statistical nature. |
| title | Efficient Stochastic Optimal Control through Approximate Bayesian Input Inference |
| topic | Machine Learning Robotics Systems and Control |
| url | https://arxiv.org/abs/2105.07693 |