Continual Reinforcement Learning

• Continual Reinforcement Learning is a framework where agents learn sequential tasks in non-stationary environments, focusing on reducing catastrophic forgetting and enhancing transfer.
• It employs methodologies such as weight preservation, replay strategies, and dynamic resource allocation to balance stability with plasticity across tasks.
• Evaluation in CRL leverages diverse benchmarks and metrics like average performance, forgetting rates, and transfer efficiency to drive lifelong learning improvements.

Continual Reinforcement Learning (CRL) is the subfield of machine learning that studies agents designed to learn a sequence of tasks or adapt within non-stationary environments, continuously acquiring, reusing, and refining skills while minimizing catastrophic forgetting. In contrast to classical reinforcement learning, which optimizes a stationary policy for a fixed Markov Decision Process (MDP), CRL methodologies and benchmarks are explicitly concerned with adaptation, stability, plasticity, and the ability to operate in the face of task drift and environment change (Nolle et al., 19 Nov 2025, Bowling et al., 10 Apr 2025, Pan et al., 27 Jun 2025). The increasing relevance of CRL is driven by its centrality to robust AI systems for real-world applications such as autonomous driving, robotics, digital twins, and other cyber-physical systems, where agents must operate indefinitely in evolving, non-stationary environments.

1. Formalization and Foundational Distinctions

In the CRL paradigm, the agent confronts either a sequence of discrete MDPs $\{T_1, T_2, ..., T_n\}$, each with potentially unique transition kernel $P_i(s'|s,a)$ and reward function $R_i(s,a)$, or an environment that exhibits evolving, possibly adversarial non-stationarity (Nolle et al., 19 Nov 2025, Bowling et al., 10 Apr 2025, Abel et al., 2023). The performance objective is to maximize cumulative expected return across all encountered tasks, subject to efficiency and minimal degradation on previous tasks: $$\max_\theta \sum_{i=1}^k J_i(\theta) \quad \text{s.t. minimal forgetting, efficient learning on task } k.$$

Key properties distinguishing CRL from standard RL include:

• Catastrophic forgetting: Decline in performance on earlier tasks due to interference from subsequent task learning (Nolle et al., 19 Nov 2025, Pan et al., 27 Jun 2025).
• Forward transfer: Acceleration or improvement of future task learning using past knowledge.
• Backward transfer: New experience improving performance on previously encountered tasks.

CRL challenges foundational RL dogmas, arguing that the Markov assumption, focus on stationary optimal policies, and the sole reliance on cumulative discounted return are fundamentally insufficient for lifelong agents (Bowling et al., 10 Apr 2025). The necessity for continual adaptation makes episodic benchmarks and policy artifacts inadequate, motivating history-based, behavior-centric, and regret-minimization frameworks as alternatives.

2. Benchmarks and Evaluation Protocols

Contemporary CRL experimentation relies on a diversity of benchmarks designed to stress adaptation, plasticity, and stability:

• Sequential task domains: Task sequences in robotic manipulation (Continual World, Meta-World), grid navigation (MiniGrid), and vision-based 3D navigation (CRLMaze) (Nolle et al., 19 Nov 2025, Traoré et al., 2019, Lomonaco et al., 2019, Pan et al., 25 Jan 2024).
• Non-stationary environments: Benchmarks with systematically shifting reward or dynamics (lighting, textures, object changes in ViZDoom, digital twin networks, or digital twins in wireless/cyber-physical settings) (Nolle et al., 19 Nov 2025, Tong et al., 14 Jan 2025).
• Real-world and sim2real settings: Incorporating domain randomization, state representation learning, and transfer to hardware platforms (e.g., robotic navigation and factory synchronization) (Traoré et al., 2019, Tong et al., 14 Jan 2025).

Key evaluation metrics include:

• Average performance across all tasks or environments at the end of learning (Pan et al., 27 Jun 2025).
• Forgetting: Measured as the drop from peak performance to post-training performance on each task, $F_i = \max_{l < i} R_{l,i} - R_{N,i}$ (Nolle et al., 19 Nov 2025, Ahn et al., 8 Mar 2024, Pan et al., 27 Jun 2025).
• Forward and backward transfer: Quantifying positive/negative impact of prior learning on new or past tasks.
• Sample efficiency: Steps required to reach performance thresholds in each environment.

These quantitative criteria, in conjunction with qualitative analyses (e.g., skill reuse rates, modularity, generalization to unseen variations), form the backbone for comparative studies.

3. Core Methodological Approaches

CRL research encompasses a spectrum of methodological strategies:

• Weight preservation mechanisms: Regularization techniques such as Elastic Weight Consolidation (EWC) penalize updates to weights deemed important for prior tasks, as measured by Fisher information, mitigating catastrophic forgetting, but often only partially (Nolle et al., 19 Nov 2025, Khetarpal et al., 2020, Pan et al., 27 Jun 2025).
• Replay and rehearsal: Experience or generative replay methods preserve or regenerate past transitions, training on a mixture of current and historical data to maintain prior policies (Pan et al., 27 Jun 2025, Khetarpal et al., 2020, Ahn et al., 8 Mar 2024).
• Policy distillation: Compressing multiple task policies into a unified student network through KL-divergence minimization enables retention across tasks without requiring task identification at test time (Traoré et al., 2019, Ahn et al., 8 Mar 2024).
• Sparse and modular architectures: Structural decomposition (e.g., structured sparsity and dormant neuron reactivation as in SSDE) allows for efficient parameter allocation between forward-transfer and plastic adaptation, balancing rigidity and expressivity (Zheng et al., 7 Mar 2025).
• Dynamic resource allocation: Approaches such as hypernetworks dynamically generate task-specific parameters without replaying prior data, scaling memory and compute requirements favorably in lifelong scenarios (Huang et al., 2020).
• Multi-granularity and meta-learning: Frameworks leveraging hierarchical policy decomposition or LLMs for goal sub-structuring (e.g., Hi-Core), and meta-learned adaptation steps, exploit both fine- and coarse-grained knowledge transfer (Pan et al., 25 Jan 2024).
• Reset and distill strategies: Counteracting negative transfer by resetting online networks at each task switch and distilling knowledge into a separate offline policy has outperformed classical continual learning algorithms in task sequences with high transfer interference (Ahn et al., 8 Mar 2024).

Algorithmic choices are further influenced by whether the CRL scenario is task-incremental (with observable boundaries and task IDs) or task-agnostic (without explicit task demarcation), as reflected in both method design and API-level support in frameworks such as Avalanche RL and Sequoia (Lucchesi et al., 2022, Normandin et al., 2021).

4. Open Challenges and Research Questions

CRL research faces several enduring challenges, substantiated by empirical studies in sequential parking, navigation, digital twin, and manipulation environments (Nolle et al., 19 Nov 2025, Lomonaco et al., 2019, Tong et al., 14 Jan 2025, Zheng et al., 7 Mar 2025):

• Reward model brittleness and abstraction drift: Reward functions and sensory-motor representations often fail to generalize even for closely related tasks without substantial redesign, indicating a need for robust model abstractions and intrinsic reward combinations (Nolle et al., 19 Nov 2025).
• Hyperparameter sensitivity: CRL performance can vary dramatically with hyperparameter settings, which often do not transfer across tasks, underlining the importance of auto-tuning or meta-learned adaptation (Nolle et al., 19 Nov 2025, Pan et al., 27 Jun 2025).
• Neural capacity utilization and order effects: Empirical studies reveal that initial tasks can monopolize representational capacity, preventing proper acquisition of subsequent tasks and causing strong task order effects (Nolle et al., 19 Nov 2025, Zheng et al., 7 Mar 2025).
• Robustness to task order and interference: As shown in both parking and manipulation scenarios, new tasks can obliterate past performance (catastrophic forgetting) or even impede learning on the current task (negative transfer), a phenomenon only partially addressed by sharing or isolating parameters (Nolle et al., 19 Nov 2025, Ahn et al., 8 Mar 2024).
• Scalability: Most strategies struggle when scaling to long task sequences or operating under resource constraints, where architectural or memory growth is restricted (Huang et al., 2020, Zheng et al., 7 Mar 2025).

5. Theoretical Advances and Foundational Perspectives

The theoretical landscape of CRL has experienced a notable shift toward formalisms that better characterize unending adaptation (Abel et al., 2023, Bowling et al., 10 Apr 2025):

• History-based environment models: By dispensing with the stationary MDP assumption in favor of history-based formalism, CRL research now emphasizes performance over full sequences of non-repetitive experience, demanding alternative notions of behavior, hindsight-rationality (deviation regret), and evaluation (Bowling et al., 10 Apr 2025).
• Agent-centric definitions: CRL has been rigorously defined as the setting in which no stationary agent is optimal—all optimal agents must themselves be continual learners that never converge on a single fixed policy, but rather engage in perpetual adaptation (Abel et al., 2023).
• Risk-awareness: The extension of risk measures to “ergodic” forms that satisfy asymptotic plasticity and local time consistency provides a framework for lifelong learning agents subject to both expected performance and risk minimization over the long run (Rojas et al., 3 Oct 2025).
• Resource-constrained CRL: Information-theoretic analyses recast continual learning as optimization under bounded memory, computational capacity, or communicational constraints, establishing sharp trade-offs between stability and plasticity (Kumar et al., 2023).

This body of theory both challenges many standard RL evaluation practices and suggests novel algorithmic pathways for building agents with provable continual adaptation properties.

6. Software Platforms and Empirical Infrastructure

Enabling large-scale, reproducible research in CRL has motivated the development of platform libraries:

• Avalanche RL (Lucchesi et al., 2022): Provides a modular PyTorch-based API for streaming environments, training strategies, and continuous evaluation with built-in regularization (EWC, SI), experience replay, and metric tracking. Integrates with OpenAI Gym and photorealistic simulation for embodied agents.
• Sequoia (Normandin et al., 2021): Formalizes a hierarchical taxonomy of CRL settings via HM-MDPs, supporting scenario classes from continuous-drift, task-agnostic, to multi-task, and enabling easy extensibility for new settings, methods, and metrics.
• Continual Habitat-Lab and CRLMaze (Lomonaco et al., 2019, Lucchesi et al., 2022): Introduce 3D vision and embodied navigation components, supporting evaluation under a diverse array of non-stationarities.

These frameworks accelerate progress by standardizing benchmarks, protocols, and facilitating the implementation and comparison of CRL algorithms at scale.

7. Outlook: Interdisciplinary and Future Directions

Persistent deficiencies in standard deep architectures—hyperparameter fragility, finite capacity, and incomplete avoidance of forgetting—raise calls for fresh, interdisciplinary approaches. Biological nervous systems exhibit properties such as synaptic consolidation, structural plasticity, and context gating that remain underexplored in artificial agents (Nolle et al., 19 Nov 2025). Open research questions include:

• How to design abstractions of sensory inputs, actions, and rewards with lasting generality to unseen tasks,
• Developing self-tuning, task-agnostic, and meta-learned adaptation mechanisms,
• Dynamic allocation and expansion of network capacity over a non-stationary task stream,
• Theory and algorithmics for risk-aware, resource-constrained, and open-ended CRL agents,
• Integration with LLMs or knowledge bases to drive goal-formulation, exploration, and robust transfer (Pan et al., 25 Jan 2024).

Progress in these dimensions is likely to benefit from collaborations bridging deep RL, neuroscience, information theory, and software engineering, ultimately advancing towards robust, adaptive, lifelong learning agents (Nolle et al., 19 Nov 2025, Pan et al., 27 Jun 2025, Abel et al., 2023).