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07/25/2023
Offline Reinforcement Learning with On-Policy Q-Function Regularization
The core challenge of offline reinforcement learning (RL) is dealing wit...
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07/24/2023
A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning
As with any machine learning problem with limited data, effective offlin...
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06/26/2023
On Imitation in Mean-field Games
We explore the problem of imitation learning (IL) in the context of mean...
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06/23/2023
GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models
Knowledge distillation is commonly used for compressing neural networks ...
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05/31/2023
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Despite the seeming success of contemporary grounded text generation sys...
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05/26/2023
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
This paper investigates model robustness in reinforcement learning (RL) ...
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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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05/02/2023
Get Back Here: Robust Imitation by Return-to-Distribution Planning
We consider the Imitation Learning (IL) setup where expert data are not ...
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03/12/2023
Twice Regularized Markov Decision Processes: The Equivalence between Robustness and Regularization
Robust Markov decision processes (MDPs) aim to handle changing or partia...
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02/10/2023
Towards Minimax Optimality of Model-based Robust Reinforcement Learning
We study the sample complexity of obtaining an ϵ-optimal policy in Robus...
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01/31/2023
Policy Gradient for s-Rectangular Robust Markov Decision Processes
We present a novel robust policy gradient method (RPG) for s-rectangular...
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01/05/2023
Extreme Q-Learning: MaxEnt RL without Entropy
Modern Deep Reinforcement Learning (RL) algorithms require estimates of ...
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12/29/2022
Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games
Mean-field games have been used as a theoretical tool to obtain an appro...
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11/07/2022
C3PO: Learning to Achieve Arbitrary Goals via Massively Entropic Pretraining
Given a particular embodiment, we propose a novel method (C3PO) that lea...
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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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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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05/25/2022
Learning Mean Field Games: A Survey
Non-cooperative and cooperative games with a very large number of player...
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05/19/2022
Learning Energy Networks with Generalized Fenchel-Young Losses
Energy-based models, a.k.a. energy networks, perform inference by optimi...
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03/16/2022
Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act
Traditionally, Reinforcement Learning (RL) aims at deciding how to act o...
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10/19/2021
Continuous Control with Action Quantization from Demonstrations
In Reinforcement Learning (RL), discrete actions, as opposed to continuo...
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10/12/2021
Twice regularized MDPs and the equivalence between robustness and regularization
Robust Markov decision processes (MDPs) aim to handle changing or partia...
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10/04/2021
Large Batch Experience Replay
Several algorithms have been proposed to sample non-uniformly the replay...
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09/20/2021
Generalization in Mean Field Games by Learning Master Policies
Mean Field Games (MFGs) can potentially scale multi-agent systems to ext...
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08/16/2021
Implicitly Regularized RL with Implicit Q-Values
The Q-function is a central quantity in many Reinforcement Learning (RL)...
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08/12/2021
A functional mirror ascent view of policy gradient methods with function approximation
We use functional mirror ascent to propose a general framework (referred...
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06/11/2021
Offline Reinforcement Learning as Anti-Exploration
Offline Reinforcement Learning (RL) aims at learning an optimal control ...
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06/08/2021
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning
We propose to learn to distinguish reversible from irreversible actions ...
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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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06/01/2021
What Matters for Adversarial Imitation Learning?
Adversarial imitation learning has become a popular framework for imitat...
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05/25/2021
Hyperparameter Selection for Imitation Learning
We address the issue of tuning hyperparameters (HPs) for imitation learn...
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05/17/2021
Mean Field Games Flock! The Reinforcement Learning Way
We present a method enabling a large number of agents to learn how to fl...
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03/02/2021
Offline Reinforcement Learning with Pseudometric Learning
Offline Reinforcement Learning methods seek to learn a policy from logge...
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02/28/2021
Scaling up Mean Field Games with Online Mirror Descent
We address scaling up equilibrium computation in Mean Field Games (MFGs)...
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02/20/2021
How To Train Your HERON
In this paper we apply Deep Reinforcement Learning (Deep RL) and Domain ...
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02/08/2021
Adversarially Guided Actor-Critic
Despite definite success in deep reinforcement learning problems, actor-...
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12/22/2020
Self-Imitation Advantage Learning
Self-imitation learning is a Reinforcement Learning (RL) method that enc...
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07/28/2020
Munchausen Reinforcement Learning
Bootstrapping is a core mechanism in Reinforcement Learning (RL). Most a...
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07/05/2020
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications
In this paper, we deepen the analysis of continuous time Fictitious Play...
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06/23/2020
Show me the Way: Intrinsic Motivation from Demonstrations
The study of exploration in Reinforcement Learning (RL) has a long histo...
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06/10/2020
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
In recent years, on-policy reinforcement learning (RL) has been successf...
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06/08/2020
Primal Wasserstein Imitation Learning
Imitation Learning (IL) methods seek to match the behavior of an agent w...
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06/06/2020
Stable and Efficient Policy Evaluation
Policy evaluation algorithms are essential to reinforcement learning due...
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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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10/28/2019
Image-Based Place Recognition on Bucolic Environment Across Seasons From Semantic Edge Description
Most of the research effort on image-based place recognition is designed...
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10/21/2019
Momentum in Reinforcement Learning
We adapt the optimization's concept of momentum to reinforcement learnin...
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10/18/2019
On Connections between Constrained Optimization and Reinforcement Learning
Dynamic Programming (DP) provides standard algorithms to solve Markov De...
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09/04/2019
Learning Sensor Placement from Demonstration for UAV networks
This work demonstrates how to leverage previous network expert demonstra...
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07/18/2019
Credit Assignment as a Proxy for Transfer in Reinforcement Learning
The ability to transfer representations to novel environments and tasks ...
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07/07/2019
ELF: Embedded Localisation of Features in pre-trained CNN
This paper introduces a novel feature detector based only on information...
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07/04/2019
