Reinforcement Learning
Self-Labeling the Job Shop Scheduling Problem
An obstacle in applying supervised paradigms to such problems is the need for costly target solutions often produced with exact solvers.
Reinforcing LLM Agents via Policy Optimization with Action Decomposition
Beginning with the simplification of flattening all actions, we theoretically explore the discrepancies between action-level optimization and this naive token-level optimization.
Integrating Suboptimal Human Knowledge with Hierarchical Reinforcement Learning for Large-Scale Multiagent Systems
While agents' learning is data-driven, sampling from millions
SustainDC: Benchmarking for Sustainable Data Center Control Supplementary Information
E-14 F Reward Evaluation and Customization F-19 F.1 Load Shifting Penalty ( LS F-19 F.2 Default Reward Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F-19 F.3 Customization of Reward Formulations . . . . . . . . . . . . . . . . . . . . . . . Current Workload - The current workload level, which includes both flexible and non-flexible components. The data center modeled is illustrated in Figure 1. The hot air exits the cabinets and returns to the CRAH via the ceiling.
How to Solve Contextual Goal-Oriented Problems with Offline Datasets?
We conduct a novel theoretical analysis to demonstrate CODA's
