Neurocomputing
Robot Motion Planning and Control
Robot Motion Planning and Control
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
Evolving neural networks through augmenting topologies
Evolutionary Computation
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Reducing Random Fluctuations in Mutative Self-adaptation
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Evolution of Adaptive Synapses: Robots with Fast Adaptive Behavior in New Environments
Evolutionary Computation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Analysis and design of echo state networks
Neural Computation
Hierarchical heuristic forward search in Stochastic domains
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Event detection and localization in mobile robot navigation using reservoir computing
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Unsupervised learning of echo state networks: a case study in artificial embryogeny
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Simbad: an autonomous robot simulation package for education and research
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Stable trajectory generator: echo state network trained by particle swarm optimization
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
The complementary roles of allostatic and contextual control systems in foraging tasks
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
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Interested in Evolutionary Robotics, this paper focuses on the acquisition and exploitation of memory skills. The targeted task is a well-studied benchmark problem, the Tolman maze, requiring in principle the robotic controller to feature some (limited) counting abilities. An elaborate experimental setting is used to enforce the controller generality and prevent opportunistic evolution from mimicking deliberative skills through smart reactive heuristics. The paper compares the prominent NEAT approach, achieving the non-parametric optimization of Neural Nets, with the evolutionary optimization of Echo State Networks, pertaining to the recent field of Reservoir Computing. While both search spaces offer a sufficient expressivity and enable the modelling of complex dynamic systems, the latter one is amenable to robust parametric, linear optimization with Covariance Matrix Adaptation-Evolution Strategies.