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06/16/2023
Bootstrapped Representations in Reinforcement Learning
In reinforcement learning (RL), state representations are key to dealing...
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05/28/2023
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
We study the problem of temporal-difference-based policy evaluation in r...
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05/01/2023
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition
Representation learning and exploration are among the key challenges for...
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03/02/2023
Understanding plasticity in neural networks
Plasticity, the ability of a neural network to quickly change its predic...
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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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12/20/2022
Settling the Reward Hypothesis
The reward hypothesis posits that, "all of what we mean by goals and pur...
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12/06/2022
Understanding Self-Predictive Learning for Reinforcement Learning
We study the learning dynamics of self-predictive learning for reinforce...
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07/15/2022
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning
We study the multi-step off-policy learning approach to distributional R...
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06/05/2022
Learning Dynamics and Generalization in Reinforcement Learning
Solving a reinforcement learning (RL) problem poses two competing challe...
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04/20/2022
Understanding and Preventing Capacity Loss in Reinforcement Learning
The reinforcement learning (RL) problem is rife with sources of non-stat...
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11/01/2021
On the Expressivity of Markov Reward
Reward is the driving force for reinforcement-learning agents. This pape...
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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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02/27/2021
Revisiting Peng's Q(λ) for Modern Reinforcement Learning
Off-policy multi-step reinforcement learning algorithms consist of conse...
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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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11/18/2020
Counterfactual Credit Assignment in Model-Free Reinforcement Learning
Credit assignment in reinforcement learning is the problem of measuring ...
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07/13/2020
Revisiting Fundamentals of Experience Replay
Experience replay is central to off-policy algorithms in deep reinforcem...
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07/07/2020
Deep Reinforcement Learning and its Neuroscientific Implications
The emergence of powerful artificial intelligence is defining new resear...
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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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12/05/2019
Hindsight Credit Assignment
We consider the problem of efficient credit assignment in reinforcement ...
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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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10/16/2019
Adaptive Trade-Offs in Off-Policy Learning
A great variety of off-policy learning algorithms exist in the literatur...
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06/12/2019
Fast Task Inference with Variational Intrinsic Successor Features
It has been established that diverse behaviors spanning the controllable...
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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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02/21/2019
Statistics and Samples in Distributional Reinforcement Learning
We present a unifying framework for designing and analysing distribution...
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01/31/2019
A Geometric Perspective on Optimal Representations for Reinforcement Learning
This paper proposes a new approach to representation learning based on g...
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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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05/13/2018
Low-pass Recurrent Neural Networks - A memory architecture for longer-term correlation discovery
Reinforcement learning (RL) agents performing complex tasks must be able...
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04/23/2018
Distributed Distributional Deterministic Policy Gradients
This work adopts the very successful distributional perspective on reinf...
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02/22/2018
An Analysis of Categorical Distributional Reinforcement Learning
Distributional approaches to value-based reinforcement learning model th...
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10/27/2017
Distributional Reinforcement Learning with Quantile Regression
In reinforcement learning an agent interacts with the environment by tak...
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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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07/21/2017
A Distributional Perspective on Reinforcement Learning
In this paper we argue for the fundamental importance of the value distr...
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05/30/2017
