Biologically plausible multi-dimensional reinforcement learning in neural networks
Presented at the International Conference on Artificial Neural Networks, Lausanne
How does the brain learn to map multi-dimensional sensory inputs to multi-dimensional motor outputs when it can only observe single rewards for the coordinated outputs of the whole network of neurons that make up the brain? We introduce Multi-AGREL, a novel, biologically plausible multi-layer neural network model for multi-dimensional reinforcement learning. We demonstrate that Multi-AGREL can learn non-linear mappings from inputs to multi-dimensional outputs by using only scalar reward feedback. We further show that in Multi-AGREL, the changes in the connection weights follow the gradient that minimizes global prediction error, and that all information required for synaptic plasticity is locally present.
|reinforcement learning, neural networks, stochastic gradient|
|Life Sciences (theme 5), Energy (theme 4)|
|Teaching and Learning in Multi Agent Systems|
|International Conference on Artificial Neural Networks|
|This work was funded by the The Netherlands Organisation for Scientific Research (NWO); grant id nwo/612.066.826 - Teaching and Learning in Multi Agent Systems|
|Organisation||Life Sciences and Health|
Rombouts, J.O, van Ooyen, A, Roelfsema, P.R, & Bohte, S.M. (2012). Biologically plausible multi-dimensional reinforcement learning in neural networks. In Proceedings of International Conference on Artificial Neural Networks 2012 (ICANN 22) (pp. 443–450). Springer Verlag.