b'Mark Rowland'

research

∙ 06/16/2023

Bootstrapped Representations in Reinforcement Learning

In reinforcement learning (RL), state representations are key to dealing...

0 Charline Le Lan, et al. ∙

research

∙ 05/29/2023

VA-learning as a more efficient alternative to Q-learning

In reinforcement learning, the advantage function is critical for policy...

0 Yunhao Tang, et al. ∙

research

∙ 05/29/2023

DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm

Multi-step learning applies lookahead over multiple time steps and has p...

0 Yunhao Tang, et al. ∙

research

∙ 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...

0 Mark Rowland, et al. ∙

research

∙ 01/11/2023

An Analysis of Quantile Temporal-Difference Learning

We analyse quantile temporal-difference learning (QTD), a distributional...

0 Mark Rowland, et al. ∙

research

∙ 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...

0 Charline Le Lan, et al. ∙

research

∙ 12/06/2022

Understanding Self-Predictive Learning for Reinforcement Learning

We study the learning dynamics of self-predictive learning for reinforce...

0 Yunhao Tang, et al. ∙

research

∙ 09/28/2022

Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees

We consider reinforcement learning in an environment modeled by an episo...

0 Daniil Tiapkin, et al. ∙

research

∙ 08/22/2022

Learning Correlated Equilibria in Mean-Field Games

The designs of many large-scale systems today, from traffic routing envi...

0 Paul Müller, et al. ∙

research

∙ 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...

0 Yunhao Tang, et al. ∙

research

∙ 06/30/2022

Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning

We introduce DeepNash, an autonomous agent capable of learning to play t...

6 Julien Perolat, et al. ∙

research

∙ 06/05/2022

Learning Dynamics and Generalization in Reinforcement Learning

Solving a reinforcement learning (RL) problem poses two competing challe...

0 Clare Lyle, et al. ∙

research

∙ 04/20/2022

Understanding and Preventing Capacity Loss in Reinforcement Learning

The reinforcement learning (RL) problem is rife with sources of non-stat...

0 Clare Lyle, et al. ∙

research

∙ 03/30/2022

Marginalized Operators for Off-policy Reinforcement Learning

In this work, we propose marginalized operators, a new class of off-poli...

0 Yunhao Tang, et al. ∙

research

∙ 11/16/2021

Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO

Recent advances in multiagent learning have seen the introduction ofa fa...

0 Paul Müller, et al. ∙

research

∙ 06/28/2021

Evolutionary Dynamics and Φ-Regret Minimization in Games

Regret has been established as a foundational concept in online learning...

0 Georgios Piliouras, et al. ∙

research

∙ 06/24/2021

Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation

Model-agnostic meta-reinforcement learning requires estimating the Hessi...

0 Yunhao Tang, et al. ∙

research

∙ 06/11/2021

Taylor Expansion of Discount Factors

In practical reinforcement learning (RL), the discount factor used for e...

0 Yunhao Tang, et al. ∙

research

∙ 02/27/2021

Revisiting Peng's Q(λ) for Modern Reinforcement Learning

Off-policy multi-step reinforcement learning algorithms consist of conse...

0 Tadashi Kozuno, et al. ∙

research

∙ 02/25/2021

On The Effect of Auxiliary Tasks on Representation Dynamics

While auxiliary tasks play a key role in shaping the representations lea...

5 Clare Lyle, et al. ∙

research

∙ 11/18/2020

Game Plan: What AI can do for Football, and What Football can do for AI

The rapid progress in artificial intelligence (AI) and machine learning ...

11 Karl Tuyls, et al. ∙

research

∙ 07/13/2020

Revisiting Fundamentals of Experience Replay

Experience replay is central to off-policy algorithms in deep reinforcem...

5 William Fedus, et al. ∙

research

∙ 05/04/2020

Navigating the Landscape of Multiplayer Games to Probe the Drosophila of AI

Multiplayer games have a long history in being used as key testbeds for ...

11 Shayegan Omidshafiei, et al. ∙

research

∙ 02/19/2020

From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization

In this paper we investigate the Follow the Regularized Leader dynamics ...

32 Julien Perolat, et al. ∙

research

∙ 10/16/2019

Conditional Importance Sampling for Off-Policy Learning

The principal contribution of this paper is a conceptual framework for o...

12 Mark Rowland, et al. ∙

research

∙ 10/16/2019

Adaptive Trade-Offs in Off-Policy Learning

A great variety of off-policy learning algorithms exist in the literatur...

0 Mark Rowland, et al. ∙

research

∙ 09/27/2019

A Generalized Training Approach for Multiagent Learning

This paper investigates a population-based training regime based on game...

20 Paul Müller, et al. ∙

research

∙ 09/21/2019

Multiagent Evaluation under Incomplete Information

This paper investigates the evaluation of learned multiagent strategies ...

24 Mark Rowland, et al. ∙

research

∙ 05/08/2019

Meta-learning of Sequential Strategies

In this report we review memory-based meta-learning as a tool for buildi...

16 Pedro A. Ortega, et al. ∙

research

∙ 03/09/2019

Orthogonal Estimation of Wasserstein Distances

Wasserstein distances are increasingly used in a wide variety of applica...

7 Mark Rowland, et al. ∙

research

∙ 03/04/2019

α-Rank: Multi-Agent Evaluation by Evolution

We introduce α-Rank, a principled evolutionary dynamics methodology, for...

0 Shayegan Omidshafiei, et al. ∙

research

∙ 02/21/2019

Statistics and Samples in Distributional Reinforcement Learning

We present a unifying framework for designing and analysing distribution...

64 Mark Rowland, et al. ∙

research

∙ 07/01/2018

Antithetic and Monte Carlo kernel estimators for partial rankings

In the modern age, rankings data is ubiquitous and it is useful for a va...

2 María Lomelí, et al. ∙

research

∙ 04/30/2018

Gaussian Process Behaviour in Wide Deep Neural Networks

Whilst deep neural networks have shown great empirical success, there is...

0 Alexander G. de G. Matthews, et al. ∙

research

∙ 04/06/2018

Structured Evolution with Compact Architectures for Scalable Policy Optimization

We present a new method of blackbox optimization via gradient approximat...

0 Krzysztof Choromanski, et al. ∙

research

∙ 02/22/2018

An Analysis of Categorical Distributional Reinforcement Learning

Distributional approaches to value-based reinforcement learning model th...

0 Mark Rowland, et al. ∙

research

∙ 10/27/2017

Distributional Reinforcement Learning with Quantile Regression

In reinforcement learning an agent interacts with the environment by tak...

0 Will Dabney, et al. ∙

research

∙ 03/02/2017

The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings

We examine a class of embeddings based on structured random matrices wit...

0 Krzysztof Choromanski, et al. ∙

research

∙ 07/10/2016

Magnetic Hamiltonian Monte Carlo

Hamiltonian Monte Carlo (HMC) exploits Hamiltonian dynamics to construct...

0 Nilesh Tripuraneni, et al. ∙

research

∙ 11/10/2015

Black-box α-divergence Minimization

Black-box alpha (BB-α) is a new approximate inference method based on th...

0 José Miguel Hernández-Lobato, et al. ∙

Mark Rowland

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