Proxy Experience Memory in Reinforcement Learning

Updated 6 March 2026

Proxy Experience Memory is a memory architecture that compresses detailed agent interactions into functionally representative proxies for efficient decision-making.
It employs methods like kernel regression, cluster averaging, and autoencoder latent keys to reduce storage needs while retaining essential behavioral context.
This approach enhances privacy, scalability, and rapid adaptation in diverse settings including federated RL, offline learning, and multi-agent coordination.

Proxy Experience Memory is a class of memory architectures and algorithmic patterns in machine learning—most prominently in reinforcement learning (RL), large-scale retrieval, and memory-augmented agent systems—in which an agent or system encodes, stores, and retrieves compressed, functionally representative proxies of its interaction history or world experience, instead of raw or fully detailed trajectories. These proxies—varying in form, from kernelized “experience particles” to cluster-averaged policies, autoencoded latent keys, or imagination-guided latent queries—serve as a dynamic substrate for value estimation, policy learning, decision-time retrieval, and generalization across nonstationary environments and distributed agents. Proxy Experience Memory enables agents or systems to retain essential behavioral context while controlling storage and computational costs, supporting privacy, scalability, and rapid adaptation to task drift.

1. Foundational Formalisms

In generalized RL frameworks, Proxy Experience Memory is instantiated as a repository of parametric proxies—termed "experience particles"—each encoding a compact summary of an agent’s interaction in the joint state–action space. Formally, at time $t$ , the $i$ th experience particle is

$w_{i,t} = (x_{i,t}, q_{i,t}),\quad x_{i,t} = (s_{i,t}, a_{i,t})\in\mathcal{S} \times \mathcal{A}^+,$

where $q_{i,t}=Q^+(x_{i,t})$ is a scalar fitness or value estimate (Chiu et al., 2022). Instead of archiving every raw transition, the agent maintains only this reduced set, which implicitly defines a reproducing-kernel Hilbert space (RKHS) embedding of experience. Each query (real or hypothetical state-action pair $x$ ) is evaluated via kernel regression over the particles: $\hat{Q}^+(x) = k(x, X)^\top K^{-1} q,$ where $X$ and $q$ stack the stored $x_i$ and $q_i$ , and $K_{ij}=k(x_i,x_j)$ is a chosen kernel. Particles are dynamically selected and updated—via mechanisms tied to temporal-difference (TD) errors and kernel similarity—to ensure that the memory remains both explanatory and minimal.

In distributed RL and federated settings, Proxy Experience Memory (or "Proxy Experience Replay Memory," ProxRM) takes the form of cluster-based, state-aggregated policy summaries (Cha et al., 2020, Cha et al., 2019). The system defines a clustering or partition $\mathcal{C}=\{C_j\}$ of the state space, computes locally averaged policy statistics

$\bar{\pi}_i(s_j^p) = \frac{1}{|\mathcal{T}_{i,j}|}\sum_{t\in\mathcal{T}_{i,j}}\pi_{\theta_i}(\cdot|s_t),$

for each proxy state (cluster centroid) $s_j^p$ , and exchanges only this anonymized, reduced proxy set, achieving a high degree of privacy and communication efficiency.

2. Architectural Variants and Mechanisms

Proxy Experience Memory exhibits substantial architectural diversity, shaped by application, environmental nonstationarity, and system constraints:

Kernel-based Experience Particle Memory: In the Generalized RL (GRL) framework (Chiu et al., 2022), experience particles are processed into a dynamic reinforcement field, which is both an ensemble function approximator and a memory device. The working memory retains only the most informative, polarized particles (with respect to TD signals), updated via kernel-similarity-constrained mechanisms that replace weak or redundant entries.
Cluster-Aggregated Proxy Memory (ProxRM): In federated reinforcement distillation, Proxy Experience Memory aggregates local experience into averaged policies on server-defined proxy states (Cha et al., 2020, Cha et al., 2019). Each agent partitions the state space, records time-averaged policies for each cluster, and only these compressed proxies are exchanged and integrated via cross-entropy loss minimization. Extensions such as mixup-augmented ProxRM interpolate between adjacent proxies to further enrich replay diversity (Cha et al., 2020).
Autoencoder Latent Key Buffers: In offline RL, Re:Frame introduces a fixed associative memory buffer (AMB) of autoencoded latent keys drawn from expert trajectories (Zelezetsky et al., 26 Aug 2025). During training on low-quality data, the agent projects its current context into this latent space, retrieves expert candidates via nearest-neighbor search, and integrates the retrieved information as an additive correction to the policy backbone.
World Model-Imagination Querying: In vision-and-language navigation and continual learning, mechanisms such as Memoir employ a learned world model to "imagine" future latent states, which serve as proxy queries into structured memory banks of past observations and behavioral patterns (Xu et al., 9 Oct 2025). This enables hybrid retrieval of both environment and behavioral history for informed policy augmentation.
Experience Memory in Multi-Agent Planning: StackPlanner utilizes structured, nonparametric experience memory as a cross-task repository of factual, procedural, and user-specific coordination patterns, directly retrievable via text-based search and serving as a proxy for long-horizon coordination experience (Zhang et al., 9 Jan 2026).
Proxy Reasoner Mediated Retrieval: In LLM memory management, frameworks such as MemSifter offload memory retrieval reasoning to a lightweight proxy model, which "sifts" raw historical segments to select those most relevant for working LLM inference, optimizing the proxy parameters via reinforcement learning on downstream outcome reward (Tan et al., 3 Mar 2026).
Latent Map Proxies in Navigation: Memory Proxy Maps (MPMs) in visual navigation accumulate a latent occupancy map derived from self-supervised feature embeddings, operating as a lightweight, experience-derived substitute for explicit 3D/metric/topological maps (2411.09893).

3. Functional Properties and Updating Protocols

Proxy Experience Memory architectures share several functional properties:

Compression and Anonymization: By design, proxies coarsen or abstract from the raw experience stream, reducing storage and eliminating direct privacy risks (notably in federated RL) (Cha et al., 2020, Cha et al., 2019).
Self-Organization: Memory update protocols ensure that only functionally valuable proxies persist. In GRL, only those particles that increase the informativeness of the reinforcement field are retained, dynamically purging obsolete or redundant proxies as the environment drifts (Chiu et al., 2022).
Associative Retrieval: The proxy repository acts as a content-addressable memory. Queries—either as next-step latent states, projected current contexts, or cluster proxies—are matched against stored proxies using kernel similarity, autoencoder latent distance, or text embedding similarity, enabling efficient policy or answer augmentation (Zelezetsky et al., 26 Aug 2025, Chiu et al., 2022, Xu et al., 9 Oct 2025, Zhang et al., 9 Jan 2026).
Multistage Aggregation: In multi-agent and federated contexts, local proxies are aggregated into a global proxy set (e.g., federated-averaged policy tables) and then broadcast back for supervised imitation or policy distillation (Cha et al., 2020, Cha et al., 2019).
Dynamic Enrichment and Robustness Mechanisms: Proxies may be diversified via mixup interpolation (Cha et al., 2020), spectral clustering on associative graphs (Chiu et al., 2022), or curriculum-driven reinforcement of the proxy retriever model (Tan et al., 3 Mar 2026).

4. Theoretical and Empirical Characteristics

Various works provide empirical and theoretical insights regarding proxy memory efficacy:

Adaptation and Generalization: Proxy Experience Memory enables rapid adaptation to nonstationarity through continual refresh and selection of relevant proxies, maintaining prediction fidelity in drifting domains (GRL) (Chiu et al., 2022).
Communication and Sample Efficiency: In federated settings, proxies reduce communication cost by up to 50% compared to full experience memory and maintain asymptotically equivalent (or superior) policy learning curves, provided cluster granularity is chosen appropriately (Cha et al., 2020, Cha et al., 2019).
Privacy: By only transmitting anonymized cluster indices and averaged policies, proxies offer statistical indistinguishability of individual experience, acting as a coarse-grained privacy mechanism (Cha et al., 2019).
Retrieval-Driven Policy Gains: Associative or proxy-based memories yield tangible performance gains when expert proxy data is scarce. For example, Re:Frame demonstrates improvements up to +10.7 normalized points using only 0.1% of expert data in D4RL MuJoCo offline RL tasks (Zelezetsky et al., 26 Aug 2025); Memoir reports 5.4% SPL increase on IR2R navigation benchmarks and 74% reduction in inference memory (Xu et al., 9 Oct 2025).
Failure Modes and Limitations: Explicit memory quantization or proxy coarsening can introduce representational errors if clusters are too broad. Small expert proxy buffers may lead to unreliable retrieval and performance collapse in high-variance domains (Zelezetsky et al., 26 Aug 2025). Static clustering may be suboptimal in highly nonstationary or high-dimensional state spaces (Cha et al., 2019).

5. Applications Across Modalities and Domains

Proxy Experience Memory underpins a wide spectrum of applied systems:

Distributed/Federated RL: Efficient, privacy-preserving distributed policy distillation among agents (Cha et al., 2020, Cha et al., 2019).
Offline/Imitation RL: Data-efficient use of limited expert trajectories in large suboptimal datasets (Zelezetsky et al., 26 Aug 2025).
Vision-and-Language Navigation: Imagination-guided retrieval from hybrid viewpoint-level memory for persistent navigation tasks (Xu et al., 9 Oct 2025).
Long-term LLM Memory: Outcome-optimized lightweight proxy models for scalable retrieval in long-horizon, memory-intensive LLM applications (Tan et al., 3 Mar 2026).
Hierarchical Multi-Agent Orchestration: Nonparametric, text-based proxy memory for recurring factual/procedural templates, improving cross-task transfer and reducing context bloat (Zhang et al., 9 Jan 2026).
Visual Navigation Without Metric Maps: Compact latent occupancy maps (MPMs) replacing resource-intensive mapping for robust image-goal navigation (2411.09893).

A selection of architectures and their proxy memory instantiations is summarized:

System/Domain	Proxy Form	Key Function
GRL (Chiu et al., 2022)	Kernel exp. particles	Value field, adaptive recall
FRD (Cha et al., 2020)	Cluster-avg. policies	Privacy, comm. reduction
Re:Frame (Zelezetsky et al., 26 Aug 2025)	AE latent keys	Associative expert retrieval
Memoir (Xu et al., 9 Oct 2025)	Imagined latent queries	Imagination-guided recall
StackPlanner (Zhang et al., 9 Jan 2026)	Text procedural proxies	Cross-task orchestration
MemSifter (Tan et al., 3 Mar 2026)	LLM proxy retriever	Outcome-driven session ranking
FeudalNav (2411.09893)	Latent occupancy map	Lightweight navigation memory

6. Open Challenges and Future Directions

Several open questions and active research directions pertain to Proxy Experience Memory:

Optimal Proxy Representation: Adaptive or learned proxy-state formation versus static clustering (e.g., vector quantization, autoencoder embedding) remains an area for exploration, particularly for highly dynamic or structured domains (Cha et al., 2019, Zelezetsky et al., 26 Aug 2025).
Formal Privacy Guarantees: While proxies anonymize experience, differential privacy or information-theoretic quantification of privacy leakage risk remains unformalized (Cha et al., 2019).
Scalable and Differentiable Updating: Efficient gradient-based updating of latent proxy sets, especially under resource constraints and large-scale multi-agent settings, requires further methodological advances.
Cross-Modal and Multimodal Proxies: Integration of proxy memories spanning vision, language, and action spaces for complex decision-making in multi-modal environments is an emergent area (Xu et al., 9 Oct 2025, 2411.09893, Zhang et al., 9 Jan 2026).
Task-outcome-driven Memory: End-to-end credit assignment to proxy retriever models on real downstream utility, as in MemSifter, provides a template for optimizing not only for retrieval accuracy but for final task value (Tan et al., 3 Mar 2026).

A plausible implication is that further advances in learning proxy representations and their updating policies—potentially unified across RL, retrieval, and generative models—will yield memory architectures with greater efficiency, privacy, and adaptability in lifelong and cooperative AI systems.