Experience Memory in Learning Systems

• Experience memory is the structured storage and retrieval of interaction histories that supports continual, online, and reinforcement learning.
• It employs data structures like buffers, graphs, and adaptive sampling policies to mitigate catastrophic forgetting and optimize credit assignment.
• Biological and computational models leverage experience memory to enhance sensorimotor recall and enable high-level strategic reasoning in autonomous agents.

Experience memory is the principled storage, organization, and retrieval of interaction histories by autonomous agents—artificial or biological—with the dual function of supporting learning (especially continual, online, or reinforcement learning) and enabling generalization across tasks and environments. In computational learning systems, experience memory is indispensable for mitigating catastrophic forgetting, accelerating long-horizon credit assignment, and scaffolding cognitive operations from basic sensorimotor recall to high-level strategic reasoning. Its design encompasses data structures (buffers, trees, graphs, maps), memory-efficient rehearsal algorithms, adaptive sampling policies, and, at scale, temporal–semantic–relational architectures.

1. Formal Characterization and Foundations

In streaming and continual learning, experience memory is commonly formalized as a buffer $\mathcal{M}$ of capacity $K$ that holds observed data points or interaction tuples, such as $\mathcal{M} = \{(x_i, y_i)\}_{i=1}^K$ in supervised settings, or full Markov transition tuples $(s_t,a_t,r_t,s_{t+1})$ in reinforcement learning. Its primary function is to facilitate experience replay, whereby training updates incorporate both new data and stored samples to stabilize learning in non-stationary, non-iid settings and minimize catastrophic forgetting (Hayes et al., 2018):

$$\theta \leftarrow \theta - \eta \nabla_\theta \left[ L(\theta; x_t, y_t) + \sum_{(x,y)\in\mathcal{M}} L(\theta; x, y) \right]$$

In deep Q-learning and actor-critic RL, the experience memory enables off-policy updates and temporal decorrelation by uniform or prioritized sampling (Li et al., 2022, Parr, 2018). Theoretical work establishes the necessity of bounded working memory for any practical online learner under real-world constraints (Hayes et al., 2018).

2. Memory Buffer Structures and Memory-Efficient Rehearsal

Canonical experience memory relies on a fixed-size buffer, often implemented as a FIFO queue, circular array, or dictionary mapping states to value tuples. Advanced frameworks introduce more memory-efficient structures to improve coverage and learning stability without prohibitive resource growth.

Streaming Clustering Buffers: ExStream maintains per-class buffers of up to $b$ prototypes using streaming clustering with nearest-pair merges, keeping memory at $O(K b d)$ for $K$ classes and feature dimension $d$, while approaching the anti-forgetting performance of full rehearsal ($\mu_{\text{total}} \approx 0.91$ versus 0.99 for full rehearsal (Hayes et al., 2018)).

Method	Memory Usage	Relative Accuracy ($\mu_{\text{total}}$)
Reservoir Sampling	$O(b d)$	0.838
ExStream	$O(K b d)$	0.909
Full Rehearsal	$O(N d)$	0.992

Map-Based Experience Memory: GWR-R uses a graph of state prototypes and temporal edges, dynamically merging similar samples to increase pairwise distances, reducing memory size by 40–80% with minimal performance loss. Merge criteria are based on activation thresholds, habituation counters, and Euclidean/node-centered metrics (Hafez et al., 2023).

Hierarchical/Episodic Memory: Systems such as ArmarX and MemoriesDB encode episodic experience as hierarchical or graph-structured entities, indexed by time, semantic content, and relational context, supporting time-bounded, semantic, and causal queries (Peller-Konrad et al., 2022, Ward, 9 Nov 2025).

3. Sampling Policies and Adaptive Replay

Uniform sampling from memory is widely used, providing decorrelation and unbiased updates, but is suboptimal for resource-constrained or non-iid learning scenarios. Non-uniform policies have demonstrated superior performance, as biased sampling can align with stabilizing or high-utility subsets (Krutsylo, 16 Feb 2025):

$$p_i = \frac{w_i}{\sum_{j=1}^K w_j}, \quad \text{where } w_i \geq 0$$

Empirical results show that, with fixed buffer contents, arbitrary non-uniform sampling can yield up to +4.68% average accuracy gains over uniform sampling on continual-image tasks (Krutsylo, 16 Feb 2025). Further, prioritized replay, as in PER, weights samples by TD error, but incurs nontrivial hardware and latency costs, motivating associative-memory and in-memory solutions for low-latency prioritization (Li et al., 2022).

Reverse experience replay (RER) introduces temporally structured sampling—walking transitions backward along recent trajectories—to expedite value propagation in sparse-reward contexts; in benchmarks, RER+Q-learning converges up to 3x faster than uniform replay (Rotinov, 2019).

4. Experience Memory in Cognitive and Long-Horizon Agents

Experience memory extends beyond shallow replay buffers in advanced robotic and cognitive architectures. In ArmarX, episodic memories are composed of cross-referenced, multi-modal snapshots (entities) with real-time workload management (WM/LTM), compression pipelines, and introspective queries (Peller-Konrad et al., 2022). Predictive and abstraction modules use experience memory for semantic annotation, plan parameterization, and future-state extrapolation.

Temporal–semantic–relational databases such as MemoriesDB generalize experience memory, storing each entry as a vertex with time stamp, embeddings, and multigraph edges encoding relations (e.g., "reply," "summary-of"), enabling concurrent temporal, semantic, and relational reasoning for recall, reinforcement, and summarization (Ward, 9 Nov 2025).

5. Strategic and Meta-Cognitive Memory in Language and Planning Agents

Modern LLM-based agents synthesize experience traces, decisions, and high-level strategies within learnable memory graphs. These multi-layered structures abstract trajectories via finite-state automata, distill high-level strategies (meta-cognition nodes), and use reinforcement to weight their empirical utility. During inference or reinforcement learning, the system adaptively injects optimized strategy prompts to bias decisions, demonstrably accelerating task learning and improving generalization across domains (Xia et al., 11 Nov 2025).

Experience-following behavior is formalized in LLM agents: similarity in task inputs yields similar outputs if analogous executions are retrieved from memory. This property, while facilitating self-improvement, introduces vulnerabilities to error propagation and misaligned replay. Selective addition and deletion policies—using downstream utility signals—are therefore required to maintain memory quality and robust long-term agent performance (Xiong et al., 21 May 2025).

6. Memory Management, Compression, and Hardware-Efficient Designs

High-capacity and latency-efficient experience memory requires explicit memory management. In continual learning (e.g., MGSER-SAM), memory buffers may interleave reservoir-sampled hard labels and soft logits, use alignment regularizers, and integrate sharpness-aware optimization for reduced forgetting and improved generalization (+24.4% over baseline ER on S-CIFAR10 (Li et al., 2024)).

Associative hardware designs (e.g., AMPER) leverage in-memory computing primitives (TCAMs) to bypass $O(\log N)$ bottlenecks of tree-based prioritized replay, enabling latency improvements (55–270x) while sustaining DRL performance (Li et al., 2022). In distributed RL and federated learning, proxy experience memory with cluster-based state abstraction and policy averaging enables privacy-preserving knowledge transfer (Cha et al., 2019).

7. Biological and Neurocomputational Models

Experience memory is also captured in biologically plausible neural models. Layered visual memory networks with fast competitive dynamics and slow bidirectional synaptic plasticity (e.g., the model of Jitsev & von der Malsburg) self-organize parts-based representations incrementally through exposure. Such models demonstrate rapid recall, generalization, and sparse binding of parts into global identities, substantiating experience-driven memory formation at the systems neuroscience level (0905.2125).

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Experience memory thus embodies a spectrum of algorithmic, architectural, and cognitive mechanisms by which agents stably retain, organize, and utilize experience for continual adaptation and long-horizon reasoning. Its continued evolution, both in memory-efficient algorithms and hardware-software co-design, is central for lifelogging AI, robust RL, strategic LLM agents, and embodied cognition.