ProActor introduces an RL framework for proactive task scheduling that uses automated time-window annotation, proactiveness metrics, GRPO optimization, and composite rewards to improve timing while preserving action alignment.
Specifically, we initialize the process con- text using mp.get_context("spawn"), and use mp.Process to launch N worker processes and cre- ate the shared M Q within the same context
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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents
ProActor introduces an RL framework for proactive task scheduling that uses automated time-window annotation, proactiveness metrics, GRPO optimization, and composite rewards to improve timing while preserving action alignment.