# Replay-enhanced Continual Reinforcement Learning

arXiv: https://arxiv.org/abs/2311.11557 · HTML: https://mlsanity.com/paper/2311.11557/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 32 references that are in the map.
- First posted on arXiv 20 Nov 2023.

## Builds on (most cited first, 26 of 26)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Experience Replay for Continual Learning](https://mlsanity.com/paper/1811.11682/) (2018)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference](https://mlsanity.com/paper/1810.11910/) (2018)
- [Policy Distillation](https://mlsanity.com/paper/1511.06295/) (2015)
- [PathNet: Evolution Channels Gradient Descent in Super Neural Networks](https://mlsanity.com/paper/1701.08734/) (2017)
- [Selective Experience Replay for Lifelong Learning](https://mlsanity.com/paper/1802.10269/) (2018)
- [Understanding the Role of Training Regimes in Continual Learning](https://mlsanity.com/paper/2006.06958/) (2020)
- [Multi-task Deep Reinforcement Learning with PopArt](https://mlsanity.com/paper/1809.04474/) (2018)
- [Continual World: A Robotic Benchmark For Continual Reinforcement Learning](https://mlsanity.com/paper/2105.10919/) (2021)
- [Optimal Continual Learning has Perfect Memory and is NP-hard](https://mlsanity.com/paper/2006.05188/) (2020)
- [Learning values across many orders of magnitude](https://mlsanity.com/paper/1602.07714/) (2016)
- [Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic Forgetting](https://mlsanity.com/paper/1812.02464/) (2018)
- [DisCoRL: Continual Reinforcement Learning via Policy Distillation](https://mlsanity.com/paper/1907.05855/) (2019)
- [Disentangling Transfer in Continual Reinforcement Learning](https://mlsanity.com/paper/2209.13900/) (2022)
- [Class-Incremental Experience Replay for Continual Learning under Concept Drift](https://mlsanity.com/paper/2104.11861/) (2021)
- [Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation](https://mlsanity.com/paper/2109.00525/) (2021)
- [A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning](https://mlsanity.com/paper/2205.10787/) (2022)
- [Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization](https://mlsanity.com/paper/2209.00347/) (2022)

## Built on by (most cited first, 7 of 7)

- [A Survey of Continual Reinforcement Learning](https://mlsanity.com/paper/2506.21872/) (2025)
- [Solving Continual Offline RL through Selective Weights Activation on Aligned Spaces](https://mlsanity.com/paper/2410.15698/) (2024)
- [Continual Knowledge Adaptation for Reinforcement Learning](https://mlsanity.com/paper/2510.19314/) (2025)
- [Continual Diffuser (CoD): Mastering Continual Offline Reinforcement Learning with Experience Rehearsal](https://mlsanity.com/paper/2409.02512/) (2024)
- [CIDER: Continual Interactive Distillation for Embodied Reinforcement Learning](https://mlsanity.com/paper/2608.21899/) (2026)
- [Principled Fast and Meta Knowledge Learners for Continual Reinforcement Learning](https://mlsanity.com/paper/2603.00903/) (2026)
- [TSN-Affinity: Similarity-Driven Parameter Reuse for Continual Offline Reinforcement Learning](https://mlsanity.com/paper/2604.25898/) (2026)

## Abstract (arXiv)

Replaying past experiences has proven to be a highly effective approach for averting catastrophic forgetting in supervised continual learning. However, some crucial factors are still largely ignored, making it vulnerable to serious failure, when used as a solution to forgetting in continual reinforcement learning, even in the context of perfect memory where all data of previous tasks are accessible in the current task. On the one hand, since most reinforcement learning algorithms are not invariant to the reward scale, the previously well-learned tasks (with high rewards) may appear to be more salient to the current learning process than the current task (with small initial rewards). This causes the agent to concentrate on those salient tasks at the expense of generality on the current task. On the other hand, offline learning on replayed tasks while learning a new task may induce a distributional shift between the dataset and the learned policy on old tasks, resulting in forgetting. In this paper, we introduce RECALL, a replay-enhanced method that greatly improves the plasticity of existing replay-based methods on new tasks while effectively avoiding the recurrence of catastrophic forgetting in continual reinforcement learning. RECALL leverages adaptive normalization on approximate targets and policy distillation on old tasks to enhance generality and stability, respectively. Extensive experiments on the Continual World benchmark show that RECALL performs significantly better than purely perfect memory replay, and achieves comparable or better overall performance against state-of-the-art continual learning methods.