Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes

• URL: http://arxiv.org/abs/2210.03512v1
• Date: Fri, 7 Oct 2022 12:56:31 GMT
• Title: Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes
• Authors: Joe Watson and Jan Peters
• Abstract summary: We show how to achieve smoother model predictive factor control using online sequential inference. We evaluate this approach on several robot control tasks, matching to sample prior methods while also ensuring smoothness.
• Score: 39.411957858548355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Monte Carlo methods have become increasingly relevant for control of non-differentiable systems, approximate dynamics models and learning from data. These methods scale to high-dimensional spaces and are effective at the non-convex optimizations often seen in robot learning. We look at sample-based methods from the perspective of inference-based control, specifically posterior policy iteration. From this perspective, we highlight how Gaussian noise priors produce rough control actions that are unsuitable for physical robot deployment. Considering smoother Gaussian process priors, as used in episodic reinforcement learning and motion planning, we demonstrate how smoother model predictive control can be achieved using online sequential inference. This inference is realized through an efficient factorization of the action distribution and a novel means of optimizing the likelihood temperature to improve importance sampling accuracy. We evaluate this approach on several high-dimensional robot control tasks, matching the sample efficiency of prior heuristic methods while also ensuring smoothness. Simulation results can be seen at https://monte-carlo-ppi.github.io/.

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