
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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Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
Standard dynamics models for continuous control make use of feedforward ...
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Benchmarks for Deep OffPolicy Evaluation
Offpolicy evaluation (OPE) holds the promise of being able to leverage ...
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Policy Information Capacity: InformationTheoretic Measure for Task Complexity in Deep Reinforcement Learning
Progress in deep reinforcement learning (RL) research is largely enabled...
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Near Optimal Policy Optimization via REPS
Since its introduction a decade ago, relative entropy policy search (REP...
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Offline Reinforcement Learning with Fisher Divergence Critic Regularization
Many modern approaches to offline Reinforcement Learning (RL) utilize be...
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Representation Matters: Offline Pretraining for Sequential Decision Making
The recent success of supervised learning methods on ever larger offline...
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Offline Policy Selection under Uncertainty
The presence of uncertainty in policy evaluation significantly complicat...
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OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning
Reinforcement learning (RL) has achieved impressive performance in a var...
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CoinDICE: OffPolicy Confidence Interval Estimation
We study highconfidence behavioragnostic offpolicy evaluation in rein...
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Statistical Bootstrapping for Uncertainty Estimation in OffPolicy Evaluation
In reinforcement learning, it is typical to use the empirically observed...
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OffPolicy Evaluation via the Regularized Lagrangian
The recently proposed distribution correction estimation (DICE) family o...
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RL Unplugged: Benchmarks for Offline Reinforcement Learning
Offline methods for reinforcement learning have the potential to help br...
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DeploymentEfficient Reinforcement Learning via ModelBased Offline Optimization
Most reinforcement learning (RL) algorithms assume online access to the ...
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D4RL: Datasets for Deep DataDriven Reinforcement Learning
The offline reinforcement learning (RL) problem, also referred to as bat...
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Datasets for DataDriven Reinforcement Learning
The offline reinforcement learning (RL) problem, also referred to as bat...
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BRPO: Batch Residual Policy Optimization
In batch reinforcement learning (RL), one often constrains a learned pol...
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Reinforcement Learning via FenchelRockafellar Duality
We review basic concepts of convex duality, focusing on the very general...
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Imitation Learning via OffPolicy Distribution Matching
When performing imitation learning from expert demonstrations, distribut...
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AlgaeDICE: Policy Gradient from Arbitrary Experience
In many realworld applications of reinforcement learning (RL), interact...
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Behavior Regularized Offline Reinforcement Learning
In reinforcement learning (RL) research, it is common to assume access t...
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Groupbased Fair Learning Leads to Counterintuitive Predictions
A number of machine learning (ML) methods have been proposed recently to...
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Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?
Hierarchical reinforcement learning has demonstrated significant success...
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MultiAgent Manipulation via Locomotion using Hierarchical Sim2Real
Manipulation and locomotion are closely related problems that are often ...
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DualDICE: BehaviorAgnostic Estimation of Discounted Stationary Distribution Corrections
In many realworld reinforcement learning applications, access to the en...
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DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Many reinforcement learning (RL) tasks provide the agent with highdimen...
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Lyapunovbased Safe Policy Optimization for Continuous Control
We study continuous action reinforcement learning problems in which it i...
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Identifying and Correcting Label Bias in Machine Learning
Datasets often contain biases which unfairly disadvantage certain groups...
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The Laplacian in RL: Learning Representations with Efficient Approximations
The smallest eigenvectors of the graph Laplacian are wellknown to provi...
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NearOptimal Representation Learning for Hierarchical Reinforcement Learning
We study the problem of representation learning in goalconditioned hier...
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DataEfficient Hierarchical Reinforcement Learning
Hierarchical reinforcement learning (HRL) is a promising approach to ext...
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A Lyapunovbased Approach to Safe Reinforcement Learning
In many realworld reinforcement learning (RL) problems, besides optimiz...
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Smoothed Action Value Functions for Learning Gaussian Policies
Stateaction value functions (i.e., Qvalues) are ubiquitous in reinforc...
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Deep Reinforcement Learning for VisionBased Robotic Grasping: A Simulated Comparative Evaluation of OffPolicy Methods
In this paper, we explore deep reinforcement learning algorithms for vis...
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Path Consistency Learning in Tsallis Entropy Regularized MDPs
We study the sparse entropyregularized reinforcement learning (ERL) pro...
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MorphNet: Fast & Simple ResourceConstrained Structure Learning of Deep Networks
We present MorphNet, an approach to automate the design of neural networ...
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TrustPCL: An OffPolicy Trust Region Method for Continuous Control
Trust region methods, such as TRPO, are often used to stabilize policy o...
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Bridging the Gap Between Value and Policy Based Reinforcement Learning
We establish a new connection between value and policy based reinforceme...
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Improving Policy Gradient by Exploring Underappreciated Rewards
This paper presents a novel form of policy gradient for modelfree reinf...
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