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papersTODAY 04:00 UTC

Continual DQN Expansion with Curriculum Learning for Adaptive Train Scheduling

A new arXiv paper tackles the stability-plasticity dilemma in continual reinforcement learning by progressively expanding a DQN agent guided by a curriculum. The approach is applied to adaptive train scheduling, where conditions shift over time and earlier knowledge must be retained. It aims to let the agent grow more complex behaviors without overwriting what it already learned.

arXivDQNadaptive-train-schedulingcontinual-reinforcement-learningcurriculum-learningstability-plasticity-dilemma

COVERAGE · 1 REPORT · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.LGMitigating the Stability-Plasticity Dilemma in Adaptive Train Scheduling with Curriculum-Driven Continual DQN Expansion ↗TODAY 04:00 UTC

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