mlsanityData to 14 Oct 2026

Task Scheduling & Forgetting in Multi-Task Reinforcement Learning

arXiv 2503.01941 ↗First posted 3 Mar 2025cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

Reinforcement learning (RL) agents can forget tasks they have previously been trained on. There is a rich body of work on such forgetting effects in humans. Therefore we look for commonalities in the forgetting behavior of humans and RL agents across tasks and test the viability of forgetting prevention measures from learning theory in RL. We find that in many cases, RL agents exhibit forgetting curves similar to those of humans. Methods like Leitner or SuperMemo have been shown to be effective at counteracting human forgetting, but we demonstrate they do not transfer as well to RL. We identify a likely cause: asymmetrical learning and retention patterns between tasks that cannot be captured by retention-based or performance-based curriculum strategies.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 11 references that are in the map. First posted on arXiv 3 Mar 2025.

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7 of 11 references.

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  1. 2024Refining Minimax Regret for Unsupervised Environment Design19 Feb

Other references · 6

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2020Curriculum Learning for Reinforcement Learning Domains: A Framework and Surveycited by 373
  3. 2023Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Taskscited by 327
  4. 2017Teacher-Student Curriculum Learningcited by 210
  5. 2020Prioritized Level Replaycited by 198
  6. 2025Optimization Learningcited by 15

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  1. 2025Retaining by Doing: The Role of On-Policy Data in Mitigating Forgetting
  2. 2026Memory Retention Is Not Enough to Master Memory Tasks in Reinforcement Learning
  3. 2025RL's Razor: Why Online Reinforcement Learning Forgets Less
  4. 2018Experience Replay for Continual Learning
  5. 2026The World Model Remembers, the Actor Forgets: Dream Rehearsal for Continual Model-Based RL
  6. 2024Unraveling the Complexity of Memory in RL Agents: an Approach for Classification and Evaluation
  7. 2025An Empirical Study of Deep Reinforcement Learning in Continuing Tasks
  8. 2024P2DT: Mitigating Forgetting in task-incremental Learning with progressive prompt Decision Transformer