Search Results for author: Mark Rowland

Found 32 papers, 10 papers with code

Fast computation of Nash Equilibria in Imperfect Information Games

no code implementations ICML 2020 Remi Munos, Julien Perolat, Jean-Baptiste Lespiau, Mark Rowland, Bart De Vylder, Marc Lanctot, Finbarr Timbers, Daniel Hennes, Shayegan Omidshafiei, Audrunas Gruslys, Mohammad Gheshlaghi Azar, Edward Lockhart, Karl Tuyls

We introduce and analyze a class of algorithms, called Mirror Ascent against an Improved Opponent (MAIO), for computing Nash equilibria in two-player zero-sum games, both in normal form and in sequential imperfect information form.

Understanding and Preventing Capacity Loss in Reinforcement Learning

no code implementations ICLR 2022 Clare Lyle, Mark Rowland, Will Dabney

The reinforcement learning (RL) problem is rife with sources of non-stationarity, making it a notoriously difficult problem domain for the application of neural networks.

Montezuma's Revenge reinforcement-learning

Marginalized Operators for Off-policy Reinforcement Learning

no code implementations30 Mar 2022 Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko

We show that the estimates for marginalized operators can be computed in a scalable way, which also generalizes prior results on marginalized importance sampling as special cases.


Evolutionary Dynamics and $Φ$-Regret Minimization in Games

no code implementations28 Jun 2021 Georgios Piliouras, Mark Rowland, Shayegan Omidshafiei, Romuald Elie, Daniel Hennes, Jerome Connor, Karl Tuyls

Importantly, $\Phi$-regret enables learning agents to consider deviations from and to mixed strategies, generalizing several existing notions of regret such as external, internal, and swap regret, and thus broadening the insights gained from regret-based analysis of learning algorithms.

online learning

Taylor Expansion of Discount Factors

no code implementations11 Jun 2021 Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko

In practical reinforcement learning (RL), the discount factor used for estimating value functions often differs from that used for defining the evaluation objective.


MICo: Improved representations via sampling-based state similarity for Markov decision processes

1 code implementation NeurIPS 2021 Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden, Mark Rowland

We present a new behavioural distance over the state space of a Markov decision process, and demonstrate the use of this distance as an effective means of shaping the learnt representations of deep reinforcement learning agents.

Atari Games reinforcement-learning

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

no code implementations27 Feb 2021 Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel

These results indicate that Peng's Q($\lambda$), which was thought to be unsafe, is a theoretically-sound and practically effective algorithm.

Continuous Control reinforcement-learning

On The Effect of Auxiliary Tasks on Representation Dynamics

no code implementations25 Feb 2021 Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney

While auxiliary tasks play a key role in shaping the representations learnt by reinforcement learning agents, much is still unknown about the mechanisms through which this is achieved.


Revisiting Fundamentals of Experience Replay

2 code implementations ICML 2020 William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney

Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but there remain significant gaps in our understanding.

DQN Replay Dataset Q-Learning +1

Navigating the Landscape of Multiplayer Games

no code implementations4 May 2020 Shayegan Omidshafiei, Karl Tuyls, Wojciech M. Czarnecki, Francisco C. Santos, Mark Rowland, Jerome Connor, Daniel Hennes, Paul Muller, Julien Perolat, Bart De Vylder, Audrunas Gruslys, Remi Munos

Multiplayer games have long been used as testbeds in artificial intelligence research, aptly referred to as the Drosophila of artificial intelligence.

Adaptive Trade-Offs in Off-Policy Learning

no code implementations16 Oct 2019 Mark Rowland, Will Dabney, Rémi Munos

A great variety of off-policy learning algorithms exist in the literature, and new breakthroughs in this area continue to be made, improving theoretical understanding and yielding state-of-the-art reinforcement learning algorithms.


Conditional Importance Sampling for Off-Policy Learning

no code implementations16 Oct 2019 Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney

We theoretically analyse this space, and concretely investigate several algorithms that arise from this framework.


Multiagent Evaluation under Incomplete Information

1 code implementation NeurIPS 2019 Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Perolat, Michal Valko, Georgios Piliouras, Remi Munos

This paper investigates the evaluation of learned multiagent strategies in the incomplete information setting, which plays a critical role in ranking and training of agents.

Orthogonal Estimation of Wasserstein Distances

no code implementations9 Mar 2019 Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamas Sarlos, Adrian Weller

Wasserstein distances are increasingly used in a wide variety of applications in machine learning.


α-Rank: Multi-Agent Evaluation by Evolution

1 code implementation4 Mar 2019 Shayegan Omidshafiei, Christos Papadimitriou, Georgios Piliouras, Karl Tuyls, Mark Rowland, Jean-Baptiste Lespiau, Wojciech M. Czarnecki, Marc Lanctot, Julien Perolat, Remi Munos

We introduce {\alpha}-Rank, a principled evolutionary dynamics methodology, for the evaluation and ranking of agents in large-scale multi-agent interactions, grounded in a novel dynamical game-theoretic solution concept called Markov-Conley chains (MCCs).

Mathematical Proofs

Statistics and Samples in Distributional Reinforcement Learning

no code implementations21 Feb 2019 Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney

We present a unifying framework for designing and analysing distributional reinforcement learning (DRL) algorithms in terms of recursively estimating statistics of the return distribution.

Distributional Reinforcement Learning reinforcement-learning

Antithetic and Monte Carlo kernel estimators for partial rankings

no code implementations1 Jul 2018 Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani

We also present a novel variance reduction scheme based on an antithetic variate construction between permutations to obtain an improved estimator for the Mallows kernel.

Multi-Object Tracking Recommendation Systems

Gaussian Process Behaviour in Wide Deep Neural Networks

1 code implementation ICLR 2018 Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani

Whilst deep neural networks have shown great empirical success, there is still much work to be done to understand their theoretical properties.

Gaussian Processes

Structured Evolution with Compact Architectures for Scalable Policy Optimization

no code implementations ICML 2018 Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller

We present a new method of blackbox optimization via gradient approximation with the use of structured random orthogonal matrices, providing more accurate estimators than baselines and with provable theoretical guarantees.

OpenAI Gym Text-to-Image Generation

An Analysis of Categorical Distributional Reinforcement Learning

no code implementations22 Feb 2018 Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh

Distributional approaches to value-based reinforcement learning model the entire distribution of returns, rather than just their expected values, and have recently been shown to yield state-of-the-art empirical performance.

Distributional Reinforcement Learning reinforcement-learning

Uprooting and Rerooting Higher-Order Graphical Models

no code implementations NeurIPS 2017 Mark Rowland, Adrian Weller

The idea of uprooting and rerooting graphical models was introduced specifically for binary pairwise models by Weller (2016) as a way to transform a model to any of a whole equivalence class of related models, such that inference on any one model yields inference results for all others.

Distributional Reinforcement Learning with Quantile Regression

15 code implementations27 Oct 2017 Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos

In this paper, we build on recent work advocating a distributional approach to reinforcement learning in which the distribution over returns is modeled explicitly instead of only estimating the mean.

Atari Games Distributional Reinforcement Learning +1

The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings

2 code implementations NeurIPS 2017 Krzysztof Choromanski, Mark Rowland, Adrian Weller

We examine a class of embeddings based on structured random matrices with orthogonal rows which can be applied in many machine learning applications including dimensionality reduction and kernel approximation.

Dimensionality Reduction

Magnetic Hamiltonian Monte Carlo

no code implementations ICML 2017 Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard Turner

We establish a theoretical basis for the use of non-canonical Hamiltonian dynamics in MCMC, and construct a symplectic, leapfrog-like integrator allowing for the implementation of magnetic HMC.

Black-box $α$-divergence Minimization

3 code implementations10 Nov 2015 José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Daniel Hernández-Lobato, Thang Bui, Richard E. Turner

Black-box alpha (BB-$\alpha$) is a new approximate inference method based on the minimization of $\alpha$-divergences.

General Classification

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