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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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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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05/22/2023
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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02/07/2023
Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision
We introduce SPEAR-TTS, a multi-speaker text-to-speech (TTS) system that...
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01/30/2023
SingSong: Generating musical accompaniments from singing
We present SingSong, a system that generates instrumental music to accom...
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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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09/30/2022
Emergent Communication: Generalization and Overfitting in Lewis Games
Lewis signaling games are a class of simple communication games for simu...
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09/14/2022
vec2text with Round-Trip Translations
We investigate models that can generate arbitrary natural language text ...
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09/07/2022
AudioLM: a Language Modeling Approach to Audio Generation
We introduce AudioLM, a framework for high-quality audio generation with...
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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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04/27/2022
On the role of population heterogeneity in emergent communication
Populations have often been perceived as a structuring component for lan...
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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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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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11/04/2021
RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning
We introduce RLDS (Reinforcement Learning Datasets), an ecosystem for re...
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10/22/2021
Solving N-player dynamic routing games with congestion: a mean field approach
The recent emergence of navigational tools has changed traffic patterns ...
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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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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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09/20/2021
Learning Natural Language Generation from Scratch
This paper introduces TRUncated ReinForcement Learning for Language (Tru...
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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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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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05/20/2021
Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness
Sparse rewards are double-edged training signals in reinforcement learni...
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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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02/08/2021
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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10/06/2020
Supervised Seeded Iterated Learning for Interactive Language Learning
Language drift has been one of the major obstacles to train language mod...
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08/07/2020
A Machine of Few Words – Interactive Speaker Recognition with Reinforcement Learning
Speaker recognition is a well known and studied task in the speech proce...
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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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07/15/2020
The Monte Carlo Transformer: a stochastic self-attention model for sequence prediction
This paper introduces the Sequential Monte Carlo Transformer, an origina...
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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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05/29/2020
Reinforcement Learning
Reinforcement learning (RL) is a general framework for adaptive control,...
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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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03/28/2020
Countering Language Drift with Seeded Iterated Learning
Supervised learning methods excel at capturing statistical properties of...
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10/21/2019
Self-Educated Language Agent With Hindsight Experience Replay For Instruction Following
Language creates a compact representation of the world and allows the de...
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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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10/04/2019
"I'm sorry Dave, I'm afraid I can't do that" Deep Q-learning from forbidden action
The use of Reinforcement Learning (RL) is still restricted to simulation...
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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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07/04/2019