Author

Davide Scaramuzza

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#artificial intelligence Preprint Oct 2026

Adaptive Expert Guidance for Efficient On-Policy Reinforcement Learning

With massively parallel simulation, on-policy Reinforcement Learning methods such as PPO have become standard in many domains. However, learning from scratch is sample-inefficient and fails to exploit the potential existence of a suboptimal expert, such as a heuristic, a model-based controller, or a policy trained on a...

D. Affinita, Ming-Jing Xu, Rudolf Reiter et al. · 0 citations

#machine learning Preprint Sep 2026

Accelerating Reinforcement Learning via MPC Solver-Gradient Guidance for Weights-varying MPC

Solver-Gradient Guided Reinforcement Learning is proposed, a solver-sensitivity augmentation for RL-based online MPC cost-weight adaptation that reaches PPO's best closed-loop return with up to 70.6% fewer samples, and outperforms GB-PL baselines by at least 54% in closed-loop return.

Baha Zarrouki, Arslan Thobani, Jasper Hoffmann et al. · 0 citations