Sequential behavior and learning in evolved dynamical neural networks
Adaptive Behavior
Integrating reactive, sequential, and learning behavior using dynamical neural networks
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
The road sign problem revisited: handling delayed response tasks with neural robot controllers
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Levels of dynamics and adaptive behavior in evolutionary neural controllers
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Evolving integrated controllers for autonomous learning robots using dynamic neural networks
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Evolving mobile robots in simulated and real environments
Artificial Life
Evolution of Neural Architecture Fitting Environmental Dynamics
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Motor primitive and sequence self-organization in a hierarchical recurrent neural network
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Real time gesture recognition using continuous time recurrent neural networks
Proceedings of the ICST 2nd international conference on Body area networks
Exploring the T-Maze: evolving learning-like robot behaviors using CTRNNs
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Genetic representation and evolvability of modular neural controllers
IEEE Computational Intelligence Magazine
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In [8] Yamauchi and Beer explored the abilities of continuous time recurrent neural networks (CTRNNs) to display reinforcement-learning like abilities. The investigated tasks were generation and learning of short bit sequences. This "learning" came about without modifications of synaptic strengths, but simply from internal dynamics of the evolved networks. In this paper this approach will be extended to two embodied agent tasks, where simulated robots have acquire and retain "knowledge" while moving around different mazes. The evolved controllers are analyzed and the results are discussed.