MemRL enables self-evolving AI agents through reinforcement learning on episodic memory with a two-phase retrieval process that filters noise and selects high-utility strategies based on environmental feedback.
2.Update Rule.The utility is updated via the linear EMA rule with learning rateα∈(0,1): Qt+1 = (1−α)Q t +αr t
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MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory
MemRL enables self-evolving AI agents through reinforcement learning on episodic memory with a two-phase retrieval process that filters noise and selects high-utility strategies based on environmental feedback.