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02/07/2023
Layered State Discovery for Incremental Autonomous Exploration
We study the autonomous exploration (AX) problem proposed by Lim Aue...
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01/05/2023
Learning Goal-Conditioned Policies Offline with Self-Supervised Reward Shaping
Developing agents that can execute multiple skills by learning from pre-...
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12/19/2022
On the Complexity of Representation Learning in Contextual Linear Bandits
In contextual linear bandits, the reward function is assumed to be a lin...
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11/04/2022
Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler
Active learning with strong and weak labelers considers a practical sett...
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10/24/2022
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees
We study the problem of representation learning in stochastic contextual...
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10/18/2022
Contextual bandits with concave rewards, and an application to fair ranking
We consider Contextual Bandits with Concave Rewards (CBCR), a multi-obje...
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10/10/2022
Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path
We study the sample complexity of learning an ϵ-optimal policy in the St...
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10/04/2022
Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies
We consider infinite-horizon discounted Markov decision processes and st...
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03/21/2022
Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL
In reinforcement learning, the graph Laplacian has proved to be a valuab...
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01/31/2022
Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning
Recent progress in deep learning has relied on access to large and diver...
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01/30/2022
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
Computing a Gaussian process (GP) posterior has a computational cost cub...
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12/13/2021
Top K Ranking for Multi-Armed Bandit with Noisy Evaluations
We consider a multi-armed bandit setting where, at the beginning of each...
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12/02/2021
Differentially Private Exploration in Reinforcement Learning with Linear Representation
This paper studies privacy-preserving exploration in Markov Decision Pro...
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11/23/2021
Adaptive Multi-Goal Exploration
We introduce a generic strategy for provably efficient multi-goal explor...
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10/27/2021
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection
We study the role of the representation of state-action value functions ...
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10/27/2021
Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching
Learning meaningful behaviors in the absence of reward is a difficult pr...
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07/23/2021
A general sample complexity analysis of vanilla policy gradient
The policy gradient (PG) is one of the most popular methods for solving ...
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07/20/2021
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
We present DrQ-v2, a model-free reinforcement learning (RL) algorithm fo...
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06/24/2021
A Fully Problem-Dependent Regret Lower Bound for Finite-Horizon MDPs
We derive a novel asymptotic problem-dependent lower-bound for regret mi...
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06/22/2021
A Unified Framework for Conservative Exploration
We study bandits and reinforcement learning (RL) subject to a conservati...
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04/22/2021
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret
We study the problem of learning in the stochastic shortest path (SSP) s...
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04/08/2021
Leveraging Good Representations in Linear Contextual Bandits
The linear contextual bandit literature is mostly focused on the design ...
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02/22/2021
Reinforcement Learning with Prototypical Representations
Learning effective representations in image-based environments is crucia...
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12/29/2020
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs
We investigate the exploration of an unknown environment when no reward ...
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10/23/2020
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits
In the contextual linear bandit setting, algorithms built on the optimis...
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08/18/2020
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration
There has been growing progress on theoretical analyses for provably eff...
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07/13/2020
Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation
We study the exploration-exploitation dilemma in the linear quadratic re...
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07/13/2020
A Provably Efficient Sample Collection Strategy for Reinforcement Learning
A common assumption in reinforcement learning (RL) is to have access to ...
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07/10/2020
Improved Analysis of UCRL2 with Empirical Bernstein Inequality
We consider the problem of exploration-exploitation in communicating Mar...
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06/22/2020
Sketched Newton-Raphson
We propose a new globally convergent stochastic second order method. Our...
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05/23/2020
A Novel Confidence-Based Algorithm for Structured Bandits
We study finite-armed stochastic bandits where the rewards of each arm m...
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05/18/2020
Meta-learning with Stochastic Linear Bandits
We investigate meta-learning procedures in the setting of stochastic lin...
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05/06/2020
Learning Adaptive Exploration Strategies in Dynamic Environments Through Informed Policy Regularization
We study the problem of learning exploration-exploitation strategies tha...
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03/06/2020
Active Model Estimation in Markov Decision Processes
We study the problem of efficient exploration in order to learn an accur...
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02/29/2020
Learning Near Optimal Policies with Low Inherent Bellman Error
We study the exploration problem with approximate linear action-value fu...
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02/23/2020
Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification
Gaussian processes (GP) are one of the most successful frameworks to mod...
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02/10/2020
Adversarial Attacks on Linear Contextual Bandits
Contextual bandit algorithms are applied in a wide range of domains, fro...
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02/08/2020
Improved Algorithms for Conservative Exploration in Bandits
In many fields such as digital marketing, healthcare, finance, and robot...
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02/08/2020
Conservative Exploration in Reinforcement Learning
While learning in an unknown Markov Decision Process (MDP), an agent sho...
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01/30/2020
Concentration Inequalities for Multinoulli Random Variables
We investigate concentration inequalities for Dirichlet and Multinomial ...
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12/07/2019
No-Regret Exploration in Goal-Oriented Reinforcement Learning
Many popular reinforcement learning problems (e.g., navigation in a maze...
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11/01/2019
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration
We consider the exploration-exploitation dilemma in finite-horizon reinf...
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10/19/2019
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning
Effective coordination is crucial to solve multi-agent collaborative (MA...
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05/29/2019
Word-order biases in deep-agent emergent communication
Sequence-processing neural networks led to remarkable progress on many N...
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03/13/2019
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret
Gaussian processes (GP) are a popular Bayesian approach for the optimiza...
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02/28/2019
Active Exploration in Markov Decision Processes
We introduce the active exploration problem in Markov decision processes...
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12/11/2018
Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes
We introduce and analyse two algorithms for exploration-exploitation in ...
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11/27/2018
Rotting bandits are no harder than stochastic ones
In bandits, arms' distributions are stationary. This is often violated i...
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07/06/2018
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes
While designing the state space of an MDP, it is common to include state...
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03/27/2018