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09/07/2023
A State Representation for Diminishing Rewards
A common setting in multitask reinforcement learning (RL) demands that a...
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02/20/2022
Selective Credit Assignment
Efficient credit assignment is essential for reinforcement learning algo...
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08/26/2021
When should agents explore?
Exploration remains a central challenge for reinforcement learning (RL)....
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05/11/2021
Return-based Scaling: Yet Another Normalisation Trick for Deep RL
Scaling issues are mundane yet irritating for practitioners of reinforce...
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12/14/2019
Adapting Behaviour for Learning Progress
Determining what experience to generate to best facilitate learning (i.e...
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10/16/2019
Conditional Importance Sampling for Off-Policy Learning
The principal contribution of this paper is a conceptual framework for o...
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07/08/2019
General non-linear Bellman equations
We consider a general class of non-linear Bellman equations. These open ...
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04/25/2019
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning
Rather than proposing a new method, this paper investigates an issue pre...
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02/26/2019
The Termination Critic
In this work, we consider the problem of autonomously discovering behavi...
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01/30/2019
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
The ability to transfer skills across tasks has the potential to scale u...
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12/18/2018
Universal Successor Features Approximators
The ability of a reinforcement learning (RL) agent to learn about many r...
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06/20/2017
Observational Learning by Reinforcement Learning
Observational learning is a type of learning that occurs as a function o...
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03/07/2016
Learning Shared Representations in Multi-task Reinforcement Learning
We investigate a paradigm in multi-task reinforcement learning (MT-RL) i...
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06/09/2015