Abstract
Learning in the brain is associated with changes of connection strengths between neurons. Here, we consider neural networks with output units for each possible action. Training is performed by giving rewards for correct actions. A major problem in effective learning is to assign credit to units playing a decisive role in the stimulus-response mapping. Previous work suggested an attentional feedback signal in combination with a global reinforcement signal to determine plasticity at units in earlier processing levels. However, it could not learn from delayed rewards (e.g., a robot could escape from fire but not walk through it to rescue a person). Based on the AGREL framework, we developed a new attention-gated learning scheme that makes use of delayed rewards. Finally, we show a close relation to standard error backpropagation.
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Brosch, T., Schwenker, F., Neumann, H. (2013). Attention-Gated Reinforcement Learning in Neural Networks—A Unified View. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_34
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DOI: https://doi.org/10.1007/978-3-642-40728-4_34
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