Adaptive Memory Crystallization for Autonomous AI Agent Learning in Dynamic Environments
Summary: arXiv:2604.13085v1 Announce Type: cross
Abstract
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.
Overview of Adaptive Memory Crystallization
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: Represents the initial, flexible state of memory.
- Glass: A transitional phase where memories begin to stabilize.
- Crystal: The final, stable phase where memories are fully integrated.
Mathematical Framework
The memory hierarchy is governed by an Itô stochastic differential equation (SDE) whose population-level behavior is captured by an explicit Fokker-Planck equation admitting a closed-form Beta stationary distribution. This mathematical foundation allows for rigorous analysis of the crystallization process.
Key Contributions
In our study, we provide proofs of several significant aspects of the AMC framework:
- Well-posedness and Global Convergence: The crystallization SDE converges to a unique Beta stationary distribution.
- Exponential Convergence: Individual crystallization states converge to their fixed points, with explicit rates and variance bounds provided.
- Q-learning Error Bounds: We establish end-to-end Q-learning error bounds and matching memory-capacity lower bounds that link SDE parameters directly to agent performance.
Empirical Evaluation
The empirical evaluation of AMC was conducted on various tasks including:
- Meta-World MT50
- Atari 20-game sequential learning
- MuJoCo continual locomotion
Results consistently show notable improvements in several key areas:
- Forward transfer improvements of 34-43% over the strongest baseline.
- Reductions in catastrophic forgetting ranging from 67-80%.
- A 62% decrease in memory footprint, enhancing efficiency in memory usage.
Conclusion
Adaptive Memory Crystallization presents a significant advancement for autonomous AI agents operating in dynamic settings by effectively balancing the acquisition of new knowledge while preserving existing information. This innovative approach has the potential to revolutionize continual reinforcement learning and improve the performance of AI systems across various applications.