mlsanityData to 14 Oct 2026

Adaptive Memory Crystallization for Autonomous AI Agent Learning in Dynamic Environments

arXiv 2604.13085 ↗First posted 2 Apr 2026cs.LG cs.AIcited by 1

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

Abstract (arXiv)

Autonomous AI agents operating in dynamic environments face a persistent challenge: acquiring new capabilities without erasing prior knowledge. We present Adaptive Memory Crystallization (AMC), a memory architecture for progressive experience consolidation in continual reinforcement learning. AMC is conceptually inspired by the qualitative structure of synaptic tagging and capture (STC) theory, the idea that memories transition through discrete stability phases, but makes no claim to model the underlying molecular or synaptic mechanisms. AMC models memory as a continuous crystallization process in which experiences migrate from plastic to stable states according to a multi-objective utility signal. The framework introduces a three-phase memory hierarchy (Liquid--Glass--Crystal) governed by an Ito stochastic differential equation (SDE) whose population-level behavior is captured by an explicit Fokker--Planck equation admitting a closed-form Beta stationary distribution. We provide proofs of: (i) well-posedness and global convergence of the crystallization SDE to a unique Beta stationary distribution; (ii) exponential convergence of individual crystallization states to their fixed points, with explicit rates and variance bounds; and (iii) end-to-end Q-learning error bounds and matching memory-capacity lower bounds that link SDE parameters directly to agent performance. Empirical evaluation on Meta-World MT50, Atari 20-game sequential learning, and MuJoCo continual locomotion consistently shows improvements in forward transfer (+34--43% over the strongest baseline), reductions in catastrophic forgetting (67--80%), and a 62% decrease in memory footprint.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 22 of its 47 references that are in the map. First posted on arXiv 2 Apr 2026.

Builds on

22 of 47 references.

References · 22

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  4. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  5. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  6. 2017Billion-scale similarity search with GPUscited by 2,497
  7. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  8. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  9. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  10. 2017Gradient Episodic Memory for Continual Learningcited by 1,898
  11. 2016Progressive Neural Networkscited by 1,361
  12. 2019Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learningcited by 1,204
10 more
  1. 2017Hindsight Experience Replaycited by 1,136
  2. 2017Memory Aware Synapses: Learning what (not) to forgetcited by 1,105
  3. 2015Prioritized Experience Replaycited by 1,099
  4. 2020Dark Experience for General Continual Learning: a Strong, Simple Baselinecited by 911
  5. 2017PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruningcited by 898
  6. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 786
  7. 2019ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Taskscited by 783
  8. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 575
  9. 2019Online Continual Learning with Maximally Interfered Retrievalcited by 379
  10. 2017Neural Episodic Controlcited by 151

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Co-Evolving Skill Generation and Policy Optimizationcited by 7

Similar, not cited

Close in wording, no citation either way

  1. 2026RoMeRL: Balancing Feedback Coverage and the Memory-Reward Trap in Self-Evolving Agent Memory via Reduced-Order Utility States
  2. 2026Memory Retention Is Not Enough to Master Memory Tasks in Reinforcement Learning
  3. 2026Rethinking Memory as Continuously Evolving Connectivity
  4. 2024Stable Hadamard Memory: Revitalizing Memory-Augmented Agents for Reinforcement Learning
  5. 2026When Continual Learning Moves to Memory: A Study of Experience Reuse in LLM Agents
  6. 2026Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents
  7. 2026AtomMem : Learnable Dynamic Agentic Memory with Atomic Memory Operation
  8. 2026Retrieval-Driven Memory Reconsolidation for Long-Term LLM Agents