AMC models memory consolidation via a Liquid-Glass-Crystal process governed by an SDE with proven convergence to a Beta distribution, yielding 34-43% better forward transfer and 67-80% less forgetting on standard continual RL benchmarks.
Self-improving reactive agents based on reinforcement learn- ing, planning and teaching
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Adaptive Memory Crystallization for Autonomous AI Agent Learning in Dynamic Environments
AMC models memory consolidation via a Liquid-Glass-Crystal process governed by an SDE with proven convergence to a Beta distribution, yielding 34-43% better forward transfer and 67-80% less forgetting on standard continual RL benchmarks.