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curriculum-learning

topic2 events
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.

reinforcement-learning/continual-learning/scheduling/research
papersTODAY 04:00 UTC

Dreaming in Code: Curriculum Learning for Open-Ended Worlds

A revised arXiv paper proposes a method for generating training environments as code, aiming to support curriculum learning in open-ended settings where agents face a continually expanding space of tasks. It builds on prior work that uses foundation models to programmatically produce diverse environments, extending it toward structured curricula. The work is a research preprint and has not been peer-reviewed.

curriculum-learning/open-ended-learning/foundation-models/arxiv