This paper proposes a method of deep artificial neuromodulation for stochastic gradient descent, applying biological neuromodulation concepts to modify learning parameters at each layer in a deep neural network during training. The method shows adaptability to different models and new problems, demonstrating dynamic, location-specific learning strategies.
Dec 11, 2018
We use gene regulatory networks (GRNs) to modulate the parameters of the SARSA reinforcement learning algorithm, demonstrating that GRNs can be optimized to enhance learning and generalize across multiple tasks without specific problem knowledge.
Jul 11, 2015
This study uses neuromodulatory gene regulatory networks to improve reinforcement learning agents in solving the robot coverage control problem, demonstrating better generalization and learning capabilities compared to fixed parameter settings.
Aug 4, 2013