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09/19/2023
Guide Your Agent with Adaptive Multimodal Rewards
Developing an agent capable of adapting to unseen environments remains a...
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06/01/2023
StyleDrop: Text-to-Image Generation in Any Style
Pre-trained large text-to-image models synthesize impressive images with...
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05/25/2023
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
Learning from human feedback has been shown to improve text-to-image mod...
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03/02/2023
Preference Transformer: Modeling Human Preferences using Transformers for RL
Preference-based reinforcement learning (RL) provides a framework to tra...
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02/23/2023
Aligning Text-to-Image Models using Human Feedback
Deep generative models have shown impressive results in text-to-image sy...
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02/10/2023
Controllability-Aware Unsupervised Skill Discovery
One of the key capabilities of intelligent agents is the ability to disc...
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02/05/2023
Multi-View Masked World Models for Visual Robotic Manipulation
Visual robotic manipulation research and applications often use multiple...
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10/24/2022
Instruction-Following Agents with Jointly Pre-Trained Vision-Language Models
Humans are excellent at understanding language and vision to accomplish ...
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09/15/2022
HARP: Autoregressive Latent Video Prediction with High-Fidelity Image Generator
Video prediction is an important yet challenging problem; burdened with ...
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06/28/2022
Masked World Models for Visual Control
Visual model-based reinforcement learning (RL) has the potential to enab...
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05/24/2022
Reward Uncertainty for Exploration in Preference-based Reinforcement Learning
Conveying complex objectives to reinforcement learning (RL) agents often...
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03/25/2022
Reinforcement Learning with Action-Free Pre-Training from Videos
Recent unsupervised pre-training methods have shown to be effective on l...
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03/18/2022
SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning
Preference-based reinforcement learning (RL) has shown potential for tea...
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11/18/2021
Improving Transferability of Representations via Augmentation-Aware Self-Supervision
Recent unsupervised representation learning methods have shown to be eff...
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11/04/2021
B-Pref: Benchmarking Preference-Based Reinforcement Learning
Reinforcement learning (RL) requires access to a reward function that in...
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10/28/2021
URLB: Unsupervised Reinforcement Learning Benchmark
Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to s...
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10/26/2021
Towards More Generalizable One-shot Visual Imitation Learning
A general-purpose robot should be able to master a wide range of tasks a...
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08/11/2021
Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback
A promising approach to solving challenging long-horizon tasks has been ...
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07/01/2021
Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble
Recent advance in deep offline reinforcement learning (RL) has made it p...
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06/18/2021
Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments
The capability of reinforcement learning (RL) agent directly depends on ...
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06/09/2021
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training
Conveying complex objectives to reinforcement learning (RL) agents can o...
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06/02/2021
Decision Transformer: Reinforcement Learning via Sequence Modeling
We present a framework that abstracts Reinforcement Learning (RL) as a s...
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03/04/2021
Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings
Recent advances in off-policy deep reinforcement learning (RL) have led ...
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02/18/2021
State Entropy Maximization with Random Encoders for Efficient Exploration
Recent exploration methods have proven to be a recipe for improving samp...
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12/17/2020
MASKER: Masked Keyword Regularization for Reliable Text Classification
Pre-trained language models have achieved state-of-the-art accuracies on...
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10/28/2020
Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in First-person Simulated 3D Environments
First-person object-interaction tasks in high-fidelity, 3D, simulated en...
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10/26/2020
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
Model-based reinforcement learning (RL) has shown great potential in var...
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09/14/2020
Decoupling Representation Learning from Reinforcement Learning
In an effort to overcome limitations of reward-driven feature learning i...
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08/03/2020
Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning
Despite the significant progress of deep reinforcement learning (RL) in ...
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07/14/2020
Learning to Sample with Local and Global Contexts in Experience Replay Buffer
Experience replay, which enables the agents to remember and reuse experi...
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07/09/2020
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
Model-free deep reinforcement learning (RL) has been successful in a ran...
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05/14/2020
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
Model-based reinforcement learning (RL) enjoys several benefits, such as...
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04/30/2020
Reinforcement Learning with Augmented Data
Learning from visual observations is a fundamental yet challenging probl...
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03/31/2020
Regularizing Class-wise Predictions via Self-knowledge Distillation
Deep neural networks with millions of parameters may suffer from poor ge...
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10/11/2019
A Simple Randomization Technique for Generalization in Deep Reinforcement Learning
Deep reinforcement learning (RL) agents often fail to generalize to unse...
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03/29/2019
Incremental Learning with Unlabeled Data in the Wild
Deep neural networks are known to suffer from catastrophic forgetting in...
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01/31/2019
Robust Inference via Generative Classifiers for Handling Noisy Labels
Large-scale datasets may contain significant proportions of noisy (incor...
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01/28/2019
Using Pre-Training Can Improve Model Robustness and Uncertainty
Tuning a pre-trained network is commonly thought to improve data efficie...
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07/10/2018
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Detecting test samples drawn sufficiently far away from the training dis...
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04/02/2018
Hierarchical Novelty Detection for Visual Object Recognition
Deep neural networks have achieved impressive success in large-scale vis...
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11/26/2017
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
The problem of detecting whether a test sample is from in-distribution (...
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06/12/2017