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09/01/2023
Local and adaptive mirror descents in extensive-form games
We study how to learn ϵ-optimal strategies in zero-sum imperfect informa...
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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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05/29/2023
Towards a Better Understanding of Representation Dynamics under TD-learning
TD-learning is a foundation reinforcement learning (RL) algorithm for va...
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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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05/22/2023
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
Mirror descent value iteration (MDVI), an abstraction of Kullback-Leible...
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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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03/14/2023
Fast Rates for Maximum Entropy Exploration
We consider the reinforcement learning (RL) setting, in which the agent ...
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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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12/23/2022
Adapting to game trees in zero-sum imperfect information games
Imperfect information games (IIG) are games in which each player only pa...
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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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11/18/2022
Curiosity in hindsight
Consider the exploration in sparse-reward or reward-free environments, s...
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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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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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06/16/2022
BYOL-Explore: Exploration by Bootstrapped Prediction
We present BYOL-Explore, a conceptually simple yet general approach for ...
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05/27/2022
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal
In this work, we consider and analyze the sample complexity of model-fre...
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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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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
Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall
We study the problem of learning a Nash equilibrium (NE) in an imperfect...
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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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06/07/2021
Concave Utility Reinforcement Learning: the Mean-field Game viewpoint
Concave Utility Reinforcement Learning (CURL) extends RL from linear to ...
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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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02/12/2021
Bootstrapped Representation Learning on Graphs
Current state-of-the-art self-supervised learning methods for graph neur...
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01/06/2021
Geometric Entropic Exploration
Exploration is essential for solving complex Reinforcement Learning (RL)...
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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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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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08/27/2020
The Advantage Regret-Matching Actor-Critic
Regret minimization has played a key role in online learning, equilibriu...
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07/24/2020
Monte-Carlo Tree Search as Regularized Policy Optimization
The combination of Monte-Carlo tree search (MCTS) with deep reinforcemen...
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06/13/2020
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-su...
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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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04/30/2020
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
Learning a good representation is an essential component for deep reinfo...
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03/31/2020
Leverage the Average: an Analysis of Regularization in RL
Building upon the formalism of regularized Markov decision processes, we...
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03/13/2020
Taylor Expansion Policy Optimization
In this work, we investigate the application of Taylor expansions in rei...
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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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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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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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06/01/2019
Neural Replicator Dynamics
In multiagent learning, agents interact in inherently nonstationary envi...
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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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02/26/2019
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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02/20/2019
World Discovery Models
As humans we are driven by a strong desire for seeking novelty in our wo...
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01/30/2019
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
The ability to transfer skills across tasks has the potential to scale u...
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01/15/2019
Optimistic optimization of a Brownian
We address the problem of optimizing a Brownian motion. We consider a (r...
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12/18/2018
Universal Successor Features Approximators
The ability of a reinforcement learning (RL) agent to learn about many r...
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11/15/2018
Neural Predictive Belief Representations
Unsupervised representation learning has succeeded with excellent result...
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10/21/2018