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Revisiting the Role of Relearning in Semantic Dementia
Patients with semantic dementia (SD) present with remarkably consistent atrophy of neurons in the anterior temporal lobe and …
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The Challenge of Redundancy on Multi-agent Value Factorisation
In the field of cooperative multi-agent reinforcement learning (MARL), the standard paradigm is the use of centralised training and …
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Accounting for the Sequential Nature of States to Learn Representations in Reinforcement Learning
In this work, we investigate the properties of data that cause popular representation learning approaches to fail. In particular, we …
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Adaptive Online Value Function Approximation with Wavelets
Using function approximation to represent a value function is necessary for continuous and high-dimensional state spaces. Linear …
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Diverse Partner Creation with Partner Prediction for Robust K-Level Reasoning
In Multi-agent Reinforcement Learning (MARL) there has been a substantial move towards creating algorithms which can be trained to work …
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Harnessing the Wisdom of an Unreliable Crowd for Autonomous Decision Making
In Reinforcement Learning there is often a need for greater sample efficiency when learning an optimal policy, whether due to the …
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Learning Abstract and Transferable Representations for Planning
We are concerned with the question of how an agent can acquire its own representations from sensory data. We restrict our focus to …
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World Value Functions: Knowledge Representation for Multitask Reinforcement Learning
An open problem in artificial intelligence is how to learn and represent knowledge that is sufficient for a general agent that needs to …
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A Bayesian approach for Learning and Tracking Switching, Non-stationary Opponents
In many situations, agents are required to use a set of strategies (behaviors) and switch among them during the course of an …
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G3DB: A Database of Successful and Failed Grasps with RGB-D Images, Point Clouds, Mesh Models and Gripper Parameters
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