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Biologically inspired architectures for sample-efficient deep reinforcement learning
Pierre H. Richemond, Arinbjörn Kolbeinsson, Yike Guo
Deep reinforcement learning requires a heavy price in terms of sample efficiency and overparameterization in the neural networks used for function approximation. In this work, we u…
Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control
Marta Sarrico, Kai Arulkumaran, Andrea Agostinelli +2
Deep networks have enabled reinforcement learning to scale to more complex and challenging domains, but these methods typically require large quantities of training data. An altern…
Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means
Andrea Agostinelli, Kai Arulkumaran, Marta Sarrico +2
Recently, neuro-inspired episodic control (EC) methods have been developed to overcome the data-inefficiency of standard deep reinforcement learning approaches. Using non-/semi-par…
Static Activation Function Normalization
Pierre H. Richemond, Yike Guo
Recent seminal work at the intersection of deep neural networks practice and random matrix theory has linked the convergence speed and robustness of these networks with the combina…
Combining learning rate decay and weight decay with complexity gradient descent - Part I
Pierre H. Richemond, Yike Guo
The role of regularization, in the specific case of deep neural networks rather than more traditional machine learning models, is still not fully elucidated. We hypothesize t…