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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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04/20/2023
Efficient Deep Reinforcement Learning Requires Regulating Overfitting
Deep reinforcement learning algorithms that learn policies by trial-and-...
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03/09/2023
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
A compelling use case of offline reinforcement learning (RL) is to obtai...
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12/08/2022
Confidence-Conditioned Value Functions for Offline Reinforcement Learning
Offline reinforcement learning (RL) promises the ability to learn effect...
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11/28/2022
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes
The potential of offline reinforcement learning (RL) is that high-capaci...
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11/21/2022
Data-Driven Offline Decision-Making via Invariant Representation Learning
The goal in offline data-driven decision-making is synthesize decisions ...
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11/02/2022
Dual Generator Offline Reinforcement Learning
In offline RL, constraining the learned policy to remain close to the da...
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11/02/2022
Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints
Offline reinforcement learning (RL) learns policies entirely from static...
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10/11/2022
Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials
Recent progress in deep learning highlights the tremendous potential of ...
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07/11/2022
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning
Reinforcement learning (RL) algorithms hold the promise of enabling auto...
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04/12/2022
When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?
Offline reinforcement learning (RL) algorithms can acquire effective pol...
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02/17/2022
Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization
Black-box model-based optimization (MBO) problems, where the goal is to ...
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02/03/2022
How to Leverage Unlabeled Data in Offline Reinforcement Learning
Offline reinforcement learning (RL) can learn control policies from stat...
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12/09/2021
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization
Despite overparameterization, deep networks trained via supervised learn...
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10/20/2021
Data-Driven Offline Optimization For Architecting Hardware Accelerators
Industry has gradually moved towards application-specific hardware accel...
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09/22/2021
A Workflow for Offline Model-Free Robotic Reinforcement Learning
Offline reinforcement learning (RL) enables learning control policies by...
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09/16/2021
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning
Offline reinforcement learning (RL) algorithms have shown promising resu...
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07/14/2021
Conservative Objective Models for Effective Offline Model-Based Optimization
Computational design problems arise in a number of settings, from synthe...
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07/13/2021
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability
Generalization is a central challenge for the deployment of reinforcemen...
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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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02/16/2021
COMBO: Conservative Offline Model-Based Policy Optimization
Model-based algorithms, which learn a dynamics model from logged experie...
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10/27/2020
COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning
Reinforcement learning has been applied to a wide variety of robotics pr...
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10/27/2020
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning
We identify an implicit under-parameterization phenomenon in value-based...
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10/27/2020
Conservative Safety Critics for Exploration
Safe exploration presents a major challenge in reinforcement learning (R...
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10/27/2020
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL
While reinforcement learning algorithms can learn effective policies for...
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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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06/08/2020
Conservative Q-Learning for Offline Reinforcement Learning
Effectively leveraging large, previously collected datasets in reinforce...
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05/04/2020
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
In this tutorial article, we aim to provide the reader with the conceptu...
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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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03/16/2020
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction
Deep reinforcement learning can learn effective policies for a wide rang...
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12/31/2019
Reward-Conditioned Policies
Reinforcement learning offers the promise of automating the acquisition ...
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12/31/2019
Model Inversion Networks for Model-Based Optimization
In this work, we aim to solve data-driven optimization problems, where t...
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10/01/2019
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
In this paper, we aim to develop a simple and scalable reinforcement lea...
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06/03/2019
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
Off-policy reinforcement learning aims to leverage experience collected ...
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05/30/2019
Graph Normalizing Flows
We introduce graph normalizing flows: a new, reversible graph neural net...
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03/03/2019
Calibration of Encoder Decoder Models for Neural Machine Translation
We study the calibration of several state of the art neural machine tran...
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02/26/2019
