Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Evolving neural networks through augmenting topologies
Evolutionary Computation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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Animals behave adaptively in environments with multiple competing goals. Understanding of mechanisms underlying such goal-directed behavior remains a challenge for neuroscience as well as for adaptive system and machine learning research. To address this problem we developed an evolutionary model of adaptive behavior in a multi-goal stochastic environment. The proposed neuroevolutionary algorithm is based on neuron's duplication as a basic mechanism of agent's recurrent neural network development. Results of simulations demonstrate that in the course of evolution agents acquire the ability to store the short-term memory and use it in behavior with alternative actions. We found that evolution discovered two mechanisms for short-term memory. The first mechanism is integration of sensory signals and ongoing internal neural activity, resulting in emergence of cell groups specialized on alternative actions. And the second mechanism is slow neurodynamical process that makes possible to encode the previous behavioral choice.