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09/18/2023
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
In this work, we present a scalable reinforcement learning method for tr...
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06/26/2023
Supervised Pretraining Can Learn In-Context Reinforcement Learning
Large transformer models trained on diverse datasets have shown a remark...
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05/26/2023
Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation
In recent years, domains such as natural language processing and image r...
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05/24/2023
Barkour: Benchmarking Animal-level Agility with Quadruped Robots
Animals have evolved various agile locomotion strategies, such as sprint...
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05/19/2023
Multimodal Web Navigation with Instruction-Finetuned Foundation Models
The progress of autonomous web navigation has been hindered by the depen...
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03/07/2023
Foundation Models for Decision Making: Problems, Methods, and Opportunities
Foundation models pretrained on diverse data at scale have demonstrated ...
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01/31/2023
Learning Universal Policies via Text-Guided Video Generation
A goal of artificial intelligence is to construct an agent that can solv...
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12/13/2022
RT-1: Robotics Transformer for Real-World Control at Scale
By transferring knowledge from large, diverse, task-agnostic datasets, m...
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11/23/2022
Multi-Environment Pretraining Enables Transfer to Action Limited Datasets
Using massive datasets to train large-scale models has emerged as a domi...
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11/03/2022
Contrastive Value Learning: Implicit Models for Simple Offline RL
Model-based reinforcement learning (RL) methods are appealing in the off...
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11/03/2022
Oracle Inequalities for Model Selection in Offline Reinforcement Learning
In offline reinforcement learning (RL), a learner leverages prior logged...
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10/24/2022
Dichotomy of Control: Separating What You Can Control from What You Cannot
Future- or return-conditioned supervised learning is an emerging paradig...
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10/08/2022
Understanding HTML with Large Language Models
Large language models (LLMs) have shown exceptional performance on a var...
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07/27/2022
PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations
Evolution Strategy (ES) algorithms have shown promising results in train...
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06/24/2022
Joint Representation Training in Sequential Tasks with Shared Structure
Classical theory in reinforcement learning (RL) predominantly focuses on...
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05/31/2022
A Mixture-of-Expert Approach to RL-based Dialogue Management
Despite recent advancements in language models (LMs), their application ...
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05/30/2022
Multi-Game Decision Transformers
A longstanding goal of the field of AI is a strategy for compiling diver...
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05/27/2022
Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters
Motivated by the success of ensembles for uncertainty estimation in supe...
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05/22/2022
Chain of Thought Imitation with Procedure Cloning
Imitation learning aims to extract high-performance policies from logged...
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01/28/2022
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error
In this work, we study the use of the Bellman equation as a surrogate ob...
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12/23/2021
Model Selection in Batch Policy Optimization
We study the problem of model selection in batch policy optimization: gi...
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11/29/2021
Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions
Reinforcement learning (RL) agents are widely used for solving complex s...
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10/27/2021
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data
The aim in imitation learning is to learn effective policies by utilizin...
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08/04/2021
Policy Gradients Incorporating the Future
Reasoning about the future – understanding how decisions in the present ...
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05/26/2021
Provable Representation Learning for Imitation with Contrastive Fourier Features
In imitation learning, it is common to learn a behavior policy to match ...
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04/28/2021
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
Standard dynamics models for continuous control make use of feedforward ...
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03/30/2021
Benchmarks for Deep Off-Policy Evaluation
Off-policy evaluation (OPE) holds the promise of being able to leverage ...
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03/23/2021
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
Progress in deep reinforcement learning (RL) research is largely enabled...
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03/17/2021
Near Optimal Policy Optimization via REPS
Since its introduction a decade ago, relative entropy policy search (REP...
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03/14/2021
Offline Reinforcement Learning with Fisher Divergence Critic Regularization
Many modern approaches to offline Reinforcement Learning (RL) utilize be...
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02/11/2021
Representation Matters: Offline Pretraining for Sequential Decision Making
The recent success of supervised learning methods on ever larger offline...
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12/12/2020
Offline Policy Selection under Uncertainty
The presence of uncertainty in policy evaluation significantly complicat...
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10/26/2020
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning
Reinforcement learning (RL) has achieved impressive performance in a var...
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10/22/2020
CoinDICE: Off-Policy Confidence Interval Estimation
We study high-confidence behavior-agnostic off-policy evaluation in rein...
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07/27/2020
Statistical Bootstrapping for Uncertainty Estimation in Off-Policy Evaluation
In reinforcement learning, it is typical to use the empirically observed...
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07/07/2020
Off-Policy Evaluation via the Regularized Lagrangian
The recently proposed distribution correction estimation (DICE) family o...
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06/24/2020
RL Unplugged: Benchmarks for Offline Reinforcement Learning
Offline methods for reinforcement learning have the potential to help br...
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06/05/2020
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
Most reinforcement learning (RL) algorithms assume online access to the ...
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04/15/2020
D4RL: Datasets for Deep Data-Driven Reinforcement Learning
The offline reinforcement learning (RL) problem, also referred to as bat...
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04/15/2020
Datasets for Data-Driven Reinforcement Learning
The offline reinforcement learning (RL) problem, also referred to as bat...
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02/08/2020
BRPO: Batch Residual Policy Optimization
In batch reinforcement learning (RL), one often constrains a learned pol...
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01/07/2020
Reinforcement Learning via Fenchel-Rockafellar Duality
We review basic concepts of convex duality, focusing on the very general...
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12/10/2019
Imitation Learning via Off-Policy Distribution Matching
When performing imitation learning from expert demonstrations, distribut...
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12/04/2019
AlgaeDICE: Policy Gradient from Arbitrary Experience
In many real-world applications of reinforcement learning (RL), interact...
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11/26/2019
Behavior Regularized Offline Reinforcement Learning
In reinforcement learning (RL) research, it is common to assume access t...
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10/04/2019
Group-based Fair Learning Leads to Counter-intuitive Predictions
A number of machine learning (ML) methods have been proposed recently to...
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09/23/2019
Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?
Hierarchical reinforcement learning has demonstrated significant success...
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08/13/2019
Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real
Manipulation and locomotion are closely related problems that are often ...
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06/10/2019
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
In many real-world reinforcement learning applications, access to the en...
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06/06/2019
