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arXiv · 2506.02767

Accelerating Model-Based Reinforcement Learning using Non-Linear Trajectory Optimization

Marco Calì·Giulio Giacomuzzo·Ruggero Carli·Alberto Dalla Libera
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Abstract

This paper addresses the slow policy optimization convergence of Monte Carlo Probabilistic Inference for Learning Control (MC-PILCO), a state-of-the-art model-based reinforcement learning (MBRL) algorithm, by integrating it with iterative Linear Quadratic Regulator (iLQR), a fast trajectory optimization method suitable for nonlinear systems. The proposed method, Exploration-Boosted MC-PILCO (EB-MC-PILCO), leverages iLQR to generate informative, exploratory trajectories and initialize the policy, significantly reducing the number of required optimization steps. Experiments on the cart-pole task demonstrate that EB-MC-PILCO accelerates convergence compared to standard MC-PILCO, achieving up to $\bm{45.9\%}$ reduction in execution time when both methods solve the task in four trials. EB-MC-PILCO also maintains a $\bm{100\%}$ success rate across trials while solving the task faster, even in cases where MC-PILCO converges in fewer iterations.

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Marco Calì, Giulio Giacomuzzo, Ruggero Carli, Alberto Dalla Libera. 2025-06-03. Accelerating Model-Based Reinforcement Learning using Non-Linear Trajectory Optimization. https://arxiv.org/abs/2506.02767

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