Reinforcement Learning in MirrorBot

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.

@InProceedings{WMEW05a, 
 	 author =  {Weber, Cornelius and Muse, David and Elshaw, Mark I. and Wermter, Stefan},  
 	 title = {Reinforcement Learning in MirrorBot}, 
 	 booktitle = {International Conference on Artificial Neural Networks 2005},
 	 number = {},
 	 volume = {},
 	 pages = {305--310},
 	 year = {2005},
 	 month = {Sep},
 	 publisher = {Springer},
 	 doi = {10.1007/11550822_48}, 
 }