Reinforcement Learning

Reducing the Planning Horizon through Reinforcement Learning
Planning is a computationally expensive process, which can limit the reactivity of autonomous agents. Planning problems are usually …
PDF Cite
Who Should I Trust? Cautiously Learning with Unreliable Experts
An important problem in reinforcement learning is the need for greater sample efficiency. One approach to dealing with this problem is …
PDF Cite
Analyzing Reinforcement Learning Algorithms for Nitrogen Fertilizer Management in Simulated Crop Growth
Establishing intelligent crop management techniques for preserving the soil, while providing next-generational food supply for an …
PDF Cite
Combining Evolutionary Search with Behaviour Cloning for Procedurally Generated Content
In this work, we consider the problem of procedural content generation for video game levels. Prior approaches have relied on …
PDF Cite Project
World Value Functions: Knowledge Representation for Learning and Planning
We propose world value functions (WVFs), a type of goaloriented general value function that represents how to solve not just a given …
PDF Cite Project
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 …
PDF Cite
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 …
PDF Cite
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 …
PDF Cite
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 …
PDF Cite Project
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 …
PDF Cite Project