---
title: "Permanent and Transient Representations for Continual Reinforcement Learning"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.07327v1"
permalink: "https://feed.astralor.com/items/itm_1fe0efb65ff875ee7ad34062"
published: "2026-11-05T02:11:47.000Z"
score: 56
channel: "ai"
tags: ["AI Agent","Permanent","Transient Representations","Continual Reinforcement Learning","CRL","One"]
---

# Permanent and Transient Representations for Continual Reinforcement Learning

## 速览

Continual Reinforcement Learning (CRL) agents struggle to adapt to new situations while retaining prior knowledge, leading to a stability-plasticity trade-off. One approach to solving this problem is by using complement…

## 关键点

- Continual Reinforcement Learning (CRL) agents struggle to adapt to new situations while retaining prior knowledge, leading to a stability-plasticity trade-off.
- One approach to solving this problem is by using complementary learning systems: one for slow, long-term learning and another for quick, transient adaptation.
- Anand & Precup (2023) instantiated this idea using permanent and transient value functions, but used the same features in both systems.

## 全文

Continual Reinforcement Learning (CRL) agents struggle to adapt to new situations while retaining prior knowledge, leading to a stability-plasticity trade-off. One approach to solving this problem is by using complementary learning systems: one for slow, long-term learning and another for quick, transient adaptation. Anand & Precup (2023) instantiated this idea using permanent and transient value functions, but used the same features in both systems. Intuitively, long-term learning should rely more on parametric representations for broad generalization, while transient learning would be best served by non-parametric representations for situation-specific adaptation. In this paper, we explore this idea both theoretically and empirically. By introducing a novel non-parametric approximation for estimating the transient value function in complex tasks, we demonstrate that our method enables online learning and outperforms competitive baselines on image-based tasks and Craftax-Classic, underscoring the effectiveness of our system-level decomposition.

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