4.6k citations · 10k across the 22 of their papers we have counts for
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Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation
Tianhong Dai, Kai Arulkumaran, Tamara Gerbert +3
Deep reinforcement learning has the potential to train robots to perform complex tasks in the real world without requiring accurate models of the robot or its environment. A practi…
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…
AlphaStar: An Evolutionary Computation Perspective
Kai Arulkumaran, Antoine Cully, Julian Togelius
In January 2019, DeepMind revealed AlphaStar to the world-the first artificial intelligence (AI) system to beat a professional player at the game of StarCraft II-representing a mil…