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01/11/2023
An Analysis of Quantile Temporal-Difference Learning
We analyse quantile temporal-difference learning (QTD), a distributional...
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07/05/2022
An Empirical Study of Implicit Regularization in Deep Offline RL
Deep neural networks are the most commonly used function approximators i...
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10/26/2021
The Difficulty of Passive Learning in Deep Reinforcement Learning
Learning to act from observational data without active environmental int...
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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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02/25/2021
On The Effect of Auxiliary Tasks on Representation Dynamics
While auxiliary tasks play a key role in shaping the representations lea...
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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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06/14/2018
Autoregressive Quantile Networks for Generative Modeling
We introduce autoregressive implicit quantile networks (AIQN), a fundame...
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06/14/2018
Implicit Quantile Networks for Distributional Reinforcement Learning
In this work, we build on recent advances in distributional reinforcemen...
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11/14/2017
Symmetric Decomposition of Asymmetric Games
We introduce new theoretical insights into two-population asymmetric gam...
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10/06/2017
Rainbow: Combining Improvements in Deep Reinforcement Learning
The deep reinforcement learning community has made several independent i...
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06/06/2016
Unifying Count-Based Exploration and Intrinsic Motivation
We consider an agent's uncertainty about its environment and the problem...
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12/15/2015