TY - GEN
T1 - Learning how to select an action
T2 - 22nd International Conference on Artificial Neural Networks, ICANN 2012
AU - Denizdurduran, Berat
AU - Sengor, Neslihan Serap
PY - 2012
Y1 - 2012
N2 - Neurophysiological experimental results suggest that basal ganglia plays crucial role in action selection while dopamine modifies this process. There are computational models based on these experimental results for action selection. This work focuses on modification of action selection by dopamine release. In the model, a dynamical system is considered for action selection and modification of action selection process is realized by reinforcement learning. The ability of the proposed dynamical system is investigated by bifurcation analysis. Based on the results of this bifurcation analysis, the effect of reinforcement learning on action selection is discussed. The model is implemented on a mobile robot and a foraging task is realized where an exploration in an unfamiliar environment with training in the world is accomplished. Thus, this work fulfills its aim of showing the efficiency of brain-inspired computational models in controlling intelligent agents.
AB - Neurophysiological experimental results suggest that basal ganglia plays crucial role in action selection while dopamine modifies this process. There are computational models based on these experimental results for action selection. This work focuses on modification of action selection by dopamine release. In the model, a dynamical system is considered for action selection and modification of action selection process is realized by reinforcement learning. The ability of the proposed dynamical system is investigated by bifurcation analysis. Based on the results of this bifurcation analysis, the effect of reinforcement learning on action selection is discussed. The model is implemented on a mobile robot and a foraging task is realized where an exploration in an unfamiliar environment with training in the world is accomplished. Thus, this work fulfills its aim of showing the efficiency of brain-inspired computational models in controlling intelligent agents.
KW - Action Selection
KW - Basal Ganglia Circuits
KW - Bifurcation Analysis
KW - Cognitive Robotics
KW - Reinforcement Learning
UR - http://www.scopus.com/inward/record.url?scp=84867749397&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33269-2_60
DO - 10.1007/978-3-642-33269-2_60
M3 - Conference contribution
AN - SCOPUS:84867749397
SN - 9783642332685
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 474
EP - 481
BT - Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings
Y2 - 11 September 2012 through 14 September 2012
ER -