mlsanityData to 7 Oct 2026

Been There, Done That: Meta-Learning with Episodic Recall

arXiv 1805.09692 ↗First posted 24 May 2018stat.ML cs.AI cs.LGcited by 25

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Abstract (arXiv)

Meta-learning agents excel at rapidly learning new tasks from open-ended task distributions; yet, they forget what they learn about each task as soon as the next begins. When tasks reoccur - as they do in natural environments - metalearning agents must explore again instead of immediately exploiting previously discovered solutions. We propose a formalism for generating open-ended yet repetitious environments, then develop a meta-learning architecture for solving these environments. This architecture melds the standard LSTM working memory with a differentiable neural episodic memory. We explore the capabilities of agents with this episodic LSTM in five meta-learning environments with reoccurring tasks, ranging from bandits to navigation and stochastic sequential decision problems.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 May 2018.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 6; 2022: 3; 2023: 6; 2024: 0; 2025: 5; 2026: 4.

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  1. 2021Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPscited by 115
  2. 2022Transformers are Meta-Reinforcement Learnerscited by 39
  3. 2021Learning where to learn: Gradient sparsity in meta and continual learningcited by 33
  4. 2021Synthetic Returns for Long-Term Credit Assignmentcited by 27
  5. 2022Large-Scale Retrieval for Reinforcement Learningcited by 24
  6. 2021How to Train Your MAML to Excel in Few-Shot Classificationcited by 22
  7. 2023Curious Replay for Model-based Adaptationcited by 16
  8. 2021HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem Solvingcited by 15
  9. 2025Latent learning: episodic memory complements parametric learning by enabling flexible reuse of experiencescited by 14
  10. 2025Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environmentscited by 11
  11. 2023Meta-Learning Strategies through Value Maximization in Neural Networkscited by 4
  12. 2025Beyond Heuristics: A Decision-Theoretic Framework for Agent Memory Managementcited by 4
13 more
  1. 2023Meta-Learning in Spiking Neural Networks with Reward-Modulated STDPcited by 2
  2. 2023Grid Cell-Inspired Fragmentation and Recall for Efficient Map Buildingcited by 2
  3. 2026A Miniature Brain Transformer: Thalamic Gating, Hippocampal Lateralization, Amygdaloid Salience, and Prefrontal Working Memory in Attention-Coupled Latent Memorycited by 2
  4. 2019Multi-task Batch Reinforcement Learning with Metric Learningcited by 1
  5. 2022Graceful Forgetting II. Data as a Processcited by 1
  6. 2025Agentic Episodic Controlcited by 1
  7. 2025From Memories to Maps: Mechanisms of In-Context Reinforcement Learning in Transformerscited by 1
  8. 2021The Emergence of Abstract and Episodic Neurons in Episodic Meta-RLcited by 0
  9. 2023An introduction to reinforcement learning for neurosciencecited by 0
  10. 2023Reconciling Shared versus Context-Specific Information in a Neural Network Model of Latent Causescited by 0
  11. 2026Evolving Afferent Architectures: Biologically-inspired Models for Damage-Avoidance Learningcited by 0
  12. 2026CDRL: A Reinforcement Learning Framework Inspired by Cerebellar Circuits and Dendritic Computational Strategiescited by 0
  13. 2026A neural network that maintains and retrieves memories based on contextcited by 0