Adaptive Memory Crystallization for Autonomous AI Agent Learning in Dynamic Environments

TL;DR

This paper introduces Adaptive Memory Crystallization (AMC), a novel memory architecture inspired by biological theories, that improves continual reinforcement learning by consolidating experiences into stable states, reducing forgetting and memory usage.

Contribution

The paper proposes AMC, a new memory model based on stochastic differential equations, with theoretical proofs and empirical results demonstrating enhanced learning and memory efficiency.

Findings

Improves forward transfer by 34-43% over baselines.

Reduces catastrophic forgetting by 67-80%.

Decreases memory footprint by 62%.

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. 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 It\^o 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.