Objective: Current ICU enteral feeding remains sub-optimal due to limited personalization and ongoing uncertainty about appropriate calorie, protein, and fluid targets--particularly in the context of rapidly changing metabolic demands and heterogeneous responses to therapeutic interventions. This study introduces DeepEN, a novel reinforcement learning (RL)-based framework designed to dynamically personalize enteral nutrition (EN) dosing for critically ill patients using electronic health record data. Methods: DeepEN was trained on data from over 11,000 ICU patients in the MIMIC-IV database to generate 4-hourly, patient-specific targets for caloric, protein, and fluid intake. The model's state space integrates demographics, comorbidities, vital signs, laboratory measurements, and recent interventions considered relevant to nutritional management. The reward function was designed with domain expertise to balance short-term physiological and nutrition-related goals with long-term survival outcomes, reflecting real-world clinical priorities. The framework employs a dueling double deep Q-network with Conservative Q-Learning regularization to ensure safe and reliable policy learning from retrospective data. Model performance was benchmarked against both clinician-derived and guideline-based policies. Results: DeepEN outperformed both clinician and guideline-based policies, achieving a 3.7 0.17 percentage-point absolute reduction in estimated morarXiv:2510.08350v2 [cs.LG] 19 Nov 2025 tality compared with the clinician policy (18.8% vs 22.5%) and higher expected returns relative to the gold-standard guideline policy (11.89 vs 8.11). Control of key nutritional biomarkers was also improved under the learned policy.

Optimal control of the future is the next frontier for AI. Current approaches to this problem are typically rooted in either reinforcement learning (RL). While powerful, this learning framework is mathematically distinct from supervised learning, which has been the main workhorse for the recent achievements in AI. Moreover, RL typically operates in a stationary environment with episodic resets, limiting its utility to more realistic settings. Here, we extend supervised learning to address learning to control in non-stationary, reset-free environments. Using this framework, called ''Prospective Learning with Control (PL+C)'', we prove that under certain fairly general assumptions, empirical risk minimization (ERM) asymptotically achieves the Bayes optimal policy. We then consider a specific instance of prospective learning with control, foraging -- which is a canonical task for any mobile agent -- be it natural or artificial. We illustrate that modern RL algorithms fail to learn in these non-stationary reset-free environments, and even with modifications, they are orders of magnitude less efficient than our prospective foraging agents.

Reinforcement learning (RL) algorithms are typically based on optimizing a Markov Decision Process (MDP) using the optimal Bellman equation.

Instead, we propose a PbRL algorithm that directly learns from preference without requiring any reward modeling.

Despite its prevalence in real-world environments, this challenge has received limited attention in unsupervised reinforcement learning.

Empirically, we show that SinkhornDRL consistently outperforms or matches existing algorithms on the Atari games suite and particularly stands out in the multi-dimensional reward setting.

Designing effective policies for the online 3D bin packing problem (3D-BPP) has been a long-standing challenge, primarily due to the unpredictable nature of incoming box sequences and stringent physical constraints.