Summary of Operator World Models For Reinforcement Learning, by Pietro Novelli et al.
Operator World Models for Reinforcement Learningby Pietro Novelli, Marco Pratticò, Massimiliano Pontil, Carlo CilibertoFirst submitted…
Operator World Models for Reinforcement Learningby Pietro Novelli, Marco Pratticò, Massimiliano Pontil, Carlo CilibertoFirst submitted…
Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control Priorsby Emma Cramer, Bernd Frauenknecht, Ramil Sabirov,…
Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systemsby Marine Cauz, Adrien Bolland,…
Meta-Gradient Search Control: A Method for Improving the Efficiency of Dyna-style Planningby Bradley Burega, John…
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashionby Yannis Flet-Berliac, Nathan…
Learning Pareto Set for Multi-Objective Continuous Robot Controlby Tianye Shu, Ke Shang, Cheng Gong, Yang…
Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocksby Emanuel Figetakis,…
Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolutionby Wenting Chen, Jie Liu, Tommy W.S.…
Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Controlby Zifan Liu, Xinran Li,…
Mixture of Experts in a Mixture of RL settingsby Timon Willi, Johan Obando-Ceron, Jakob Foerster,…