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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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01/11/2023
An Analysis of Quantile Temporal-Difference Learning
We analyse quantile temporal-difference learning (QTD), a distributional...
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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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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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11/03/2021
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
Meaningful and simplified representations of neural activity can yield i...
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02/19/2021
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction
State-of-the-art methods for self-supervised learning (SSL) build repres...
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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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08/27/2020
The Advantage Regret-Matching Actor-Critic
Regret minimization has played a key role in online learning, equilibriu...
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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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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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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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11/15/2018
Neural Predictive Belief Representations
Unsupervised representation learning has succeeded with excellent result...
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05/29/2018
Observe and Look Further: Achieving Consistent Performance on Atari
Despite significant advances in the field of deep Reinforcement Learning...
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06/30/2017
Noisy Networks for Exploration
We introduce NoisyNet, a deep reinforcement learning agent with parametr...
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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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03/16/2017
Minimax Regret Bounds for Reinforcement Learning
We consider the problem of provably optimal exploration in reinforcement...
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02/05/2016
Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes
Finding efficient and provable methods to solve non-convex optimization ...
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02/04/2014
Online Stochastic Optimization under Correlated Bandit Feedback
In this paper we consider the problem of online stochastic optimization ...
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07/25/2013
Sequential Transfer in Multi-armed Bandit with Finite Set of Models
Learning from prior tasks and transferring that experience to improve fu...
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05/05/2013
Regret Bounds for Reinforcement Learning with Policy Advice
In some reinforcement learning problems an agent may be provided with a ...
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06/27/2012
On the Sample Complexity of Reinforcement Learning with a Generative Model
We consider the problem of learning the optimal action-value function in...
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04/12/2010
