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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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05/29/2023
VA-learning as a more efficient alternative to Q-learning
In reinforcement learning, the advantage function is critical for policy...
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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...
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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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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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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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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...
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08/22/2022
Learning Correlated Equilibria in Mean-Field Games
The designs of many large-scale systems today, from traffic routing envi...
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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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06/30/2022
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
We introduce DeepNash, an autonomous agent capable of learning to play t...
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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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03/30/2022
Marginalized Operators for Off-policy Reinforcement Learning
In this work, we propose marginalized operators, a new class of off-poli...
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11/16/2021
Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO
Recent advances in multiagent learning have seen the introduction ofa fa...
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06/28/2021
Evolutionary Dynamics and Φ-Regret Minimization in Games
Regret has been established as a foundational concept in online learning...
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06/24/2021
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation
Model-agnostic meta-reinforcement learning requires estimating the Hessi...
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06/11/2021
Taylor Expansion of Discount Factors
In practical reinforcement learning (RL), the discount factor used for e...
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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
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 ...
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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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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 ...
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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 ...
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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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09/27/2019
A Generalized Training Approach for Multiagent Learning
This paper investigates a population-based training regime based on game...
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09/21/2019
Multiagent Evaluation under Incomplete Information
This paper investigates the evaluation of learned multiagent strategies ...
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05/08/2019
Meta-learning of Sequential Strategies
In this report we review memory-based meta-learning as a tool for buildi...
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03/09/2019
Orthogonal Estimation of Wasserstein Distances
Wasserstein distances are increasingly used in a wide variety of applica...
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03/04/2019
α-Rank: Multi-Agent Evaluation by Evolution
We introduce α-Rank, a principled evolutionary dynamics methodology, for...
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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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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...
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04/30/2018
Gaussian Process Behaviour in Wide Deep Neural Networks
Whilst deep neural networks have shown great empirical success, there is...
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04/06/2018
Structured Evolution with Compact Architectures for Scalable Policy Optimization
We present a new method of blackbox optimization via gradient approximat...
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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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03/02/2017
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings
We examine a class of embeddings based on structured random matrices wit...
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07/10/2016
Magnetic Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) exploits Hamiltonian dynamics to construct...
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11/10/2015