Evolutionary Algorithm for Reservoir Learning and Yielding
Summary
Introduces EARLY, an evolutionary framework for evolving multi-reservoir Echo State Networks that outperforms random search on temporal learning tasks and exhibits task-dependent structural differences.
View Cached Full Text
Cached at: 06/01/26, 09:29 AM
# Evolutionary Algorithm for Reservoir Learning and Yielding Source: [https://arxiv.org/abs/2605.30372](https://arxiv.org/abs/2605.30372) [View PDF](https://arxiv.org/pdf/2605.30372) > Abstract:Reservoir computing, a type of recurrent neural network, is a promising approach for temporal learning as it separates dynamic processing from the trained readout layer\. However, classical Echo State Networks \(ESNs\) often require task\-specific tuning of their architecture and hyperparameters to achieve good performance\. This paper introduces EARLY \(Evolutionary Algorithm for Reservoir Learning and Yielding\), a framework designed to evolve both the topology and hyperparameters of multi\-reservoir ESNs\. Inspired by the modular organisation of the brain, EARLY encodes architectures as graph\-based genomes and applies crossover, mutation, and selection to discover effective configurations\. Our goal is to create both generic architectures and tasks inducing generalization\. The method is evaluated on temporal learning tasks from the CogScale dataset\. Results show that evolved architectures outperform those obtained with random search on several tasks and exhibit structural differences depending on task difficulty: simpler tasks yield lightweight architectures, while more complex tasks favour richer modular organisations\. These findings suggest that evolutionary search can help identify reusable reservoir structures for a broader range of temporal problems\. The evolved architectures are further evaluated on a cross\-situational learning dataset to assess their ability to adapt to new environments\. ## Submission history From: Julien Testu \[[view email](https://arxiv.org/show-email/a4cf769c/2605.30372)\] \[via CCSD proxy\] **\[v1\]**Tue, 19 May 2026 11:22:21 UTC \(4,592 KB\)
Similar Articles
EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning
EvoTrainer introduces an autonomous training framework that co-evolves LLM policies and training harnesses through empirical feedback, outperforming human-engineered RL baselines on mathematical reasoning, code generation, and long-horizon software engineering tasks.
Evolution strategies as a scalable alternative to reinforcement learning
OpenAI presents evolution strategies (ES) as a scalable black-box optimization alternative to reinforcement learning for training neural network policies. ES simplifies the optimization problem by treating policy training as a stochastic parameter search that repeatedly samples and selects better parameter configurations based on reward feedback.
Evolved Policy Gradients
OpenAI introduces Evolved Policy Gradients (EPG), a meta-learning approach that learns loss functions through evolution rather than learning policies directly, enabling RL agents to generalize better across tasks by leveraging prior experience similar to how humans transfer skills.
AEM: Adaptive Entropy Modulation for Multi-Turn Agentic Reinforcement Learning
This paper introduces AEM, a supervision-free method for agentic reinforcement learning that adapts entropy dynamics at the response level to improve exploration-exploitation trade-offs. It demonstrates performance gains on benchmarks like ALFWorld and SWE-bench by aligning uncertainty estimation with action granularity.
PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents
The paper introduces PACEvolve++, a reinforcement learning framework that improves test-time policy adaptation for evolutionary search agents by decoupling hypothesis generation from execution.