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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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04/25/2023
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks
Auxiliary tasks improve the representations learned by deep reinforcemen...
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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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12/08/2022
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces
Many machine learning problems encode their data as a matrix with a poss...
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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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06/03/2022
Beyond Tabula Rasa: Reincarnating Reinforcement Learning
Learning tabula rasa, that is without any prior knowledge, is the preval...
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05/24/2022
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning
Continuous-time reinforcement learning offers an appealing formalism for...
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03/01/2022
On the Generalization of Representations in Reinforcement Learning
In reinforcement learning, state representations are used to tractably d...
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09/22/2021
On Bonus-Based Exploration Methods in the Arcade Learning Environment
Research on exploration in reinforcement learning, as applied to Atari 2...
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08/30/2021
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Deep reinforcement learning (RL) algorithms are predominantly evaluated ...
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02/02/2021
Metrics and continuity in reinforcement learning
In most practical applications of reinforcement learning, it is untenabl...
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01/13/2021
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Reinforcement learning methods trained on few environments rarely learn ...
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09/15/2020
The Importance of Pessimism in Fixed-Dataset Policy Optimization
We study worst-case guarantees on the expected return of fixed-dataset p...
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07/10/2020
Representations for Stable Off-Policy Reinforcement Learning
Reinforcement learning with function approximation can be unstable and e...
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03/27/2020
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms
We present a distributional approach to theoretical analyses of reinforc...
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03/09/2020
Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces
Despite the wealth of research into provably efficient reinforcement lea...
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02/28/2020
On Catastrophic Interference in Atari 2600 Games
Model-free deep reinforcement learning algorithms are troubled with poor...
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11/28/2019
Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction
Text-based games are a natural challenge domain for deep reinforcement l...
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08/06/2019
Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment
This paper provides an empirical evaluation of recently developed explor...
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06/06/2019
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Many reinforcement learning (RL) tasks provide the agent with high-dimen...
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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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02/19/2019
Hyperbolic Discounting and Learning over Multiple Horizons
Reinforcement learning (RL) typically defines a discount factor as part ...
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02/08/2019
Distributional reinforcement learning with linear function approximation
Despite many algorithmic advances, our theoretical understanding of prac...
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02/01/2019
The Hanabi Challenge: A New Frontier for AI Research
From the early days of computing, games have been important testbeds for...
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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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01/31/2019
Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue
We consider the problem of designing an artificial agent capable of inte...
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01/31/2019
The Value Function Polytope in Reinforcement Learning
We establish geometric and topological properties of the space of value ...
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01/30/2019
A Comparative Analysis of Expected and Distributional Reinforcement Learning
Since their introduction a year ago, distributional approaches to reinfo...
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01/27/2019
Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift
In this paper we revisit the method of off-policy corrections for reinfo...
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12/14/2018
Dopamine: A Research Framework for Deep Reinforcement Learning
Deep reinforcement learning (deep RL) research has grown significantly i...
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11/30/2018
An Introduction to Deep Reinforcement Learning
Deep reinforcement learning is the combination of reinforcement learning...
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08/29/2018
Approximate Exploration through State Abstraction
Although exploration in reinforcement learning is well understood from a...
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07/31/2018
Count-Based Exploration with the Successor Representation
The problem of exploration in reinforcement learning is well-understood ...
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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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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
The Cramer Distance as a Solution to Biased Wasserstein Gradients
The Wasserstein probability metric has received much attention from the ...
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04/15/2017
The Reactor: A Sample-Efficient Actor-Critic Architecture
In this work we present a new reinforcement learning agent, called React...
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04/10/2017
Automated Curriculum Learning for Neural Networks
We introduce a method for automatically selecting the path, or syllabus,...
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06/08/2016
Safe and Efficient Off-Policy Reinforcement Learning
In this work, we take a fresh look at some old and new algorithms for of...
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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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02/16/2016
Q(λ) with Off-Policy Corrections
We propose and analyze an alternate approach to off-policy multi-step te...
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12/15/2015
Increasing the Action Gap: New Operators for Reinforcement Learning
This paper introduces new optimality-preserving operators on Q-functions...
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11/19/2014
Compress and Control
This paper describes a new information-theoretic policy evaluation techn...
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07/19/2012
