Reinforcement Learning

The ability to compose learned skills to solve new tasks is an important property for lifelong-learning agents. In this work we …

The ability to produce novel behaviours from existing skills is an important property of lifelong-learning agents. We build on recent …

We present a framework for autonomously learning a portable representation that describes a collection of low-level continuous …

We propose a method for autonomously learning an object-centric representation of a highdimensional environment that is suitable for …

We propose a framework for defining a Boolean algebra over the space of tasks. This allows us to formulate new tasks in terms of the …

We present a method for learning options from segmented demonstration trajectories. The trajectories are first segmented into skills …

An important property for lifelong-learning agents is the ability to combine existing skills to solve new unseen tasks. In general, …

Parameterised actions in reinforcement learning are composed of discrete actions with continuous action-parameters. This provides a …

We present a framework for autonomously learning a portable symbolic representation that describes a collection of low-level continuous …

An important property for lifelong-learning agents is the ability to combine existing skills to solve unseen tasks. In general, …