Module-Based Reinforcement Learning: Experiments with a Real Robot
Machine Learning - Special issue on learning in autonomous robots
Self-Organizing Maps
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Adaptive state space partitioning for reinforcement learning
Engineering Applications of Artificial Intelligence
A distributed model of spatial visual attention
Biomimetic Neural Learning for Intelligent Robots
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For this special session of EU projects in the area of NeuroIT, we will review the progress of the MirrorBot project with special emphasis on its relation to reinforcement learning and future perspectives. Models inspired by mirror neurons in the cortex, while enabling a system to understand its actions, also help in the solving of the curse of dimensionality problem of reinforcement learning. Reinforcement learning, which is primarily linked to the basal ganglia, is a powerful method to teach an agent such as a robot a goal-directed action strategy. Its limitation is mainly that the perceived situation has to be mapped to a state space, which grows exponentially with input dimensionality. Cortex-inspired computation can alleviate this problem by pre-processing sensory information and supplying motor primitives that can act as modules for a superordinate reinforcement learning scheme.