Reinforcement Learning

Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces
Parameterised actions in reinforcement learning are composed of discrete actions with continuous action-parameters. This provides a …
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Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning
Object-oriented representations in reinforcement learning have shown promise in transfer learning, with previous research introducing a …
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Learning to Plan with Portable Symbols
We present a framework for autonomously learning a portable symbolic representation that describes a collection of low-level continuous …
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Will it Blend? Composing Value Functions in Reinforcement Learning
An important property for lifelong-learning agents is the ability to combine existing skills to solve unseen tasks. In general, …
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Hierarchical Subtask Discovery with Non-negative Matrix Factorization
Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains. However, …
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Belief Reward Shaping in Reinforcement Learning
A key challenge in many reinforcement learning problems is delayed rewards, which can significantly slow down learning. Although reward …
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Online Constrained Model-based Reinforcement Learning
Applying reinforcement learning to robotic systems poses a number of challenging problems. A key requirement is the ability to handle …
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Hierarchical Subtask Discovery with Non-negative Matrix Factorization
Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains. However, …
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Hierarchy Through Composition with Multitask LMDPs
Hierarchical architectures are critical to the scalability of reinforcement learning methods. Most current hierarchical frameworks …
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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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