research
∙
09/20/2023
Safe and Robust Multi-Agent Reinforcement Learning for Connected Autonomous Vehicles under State Perturbations
Sensing and communication technologies have enhanced learning-based deci...
share
research
∙
09/07/2023
Equal Long-term Benefit Rate: Adapting Static Fairness Notions to Sequential Decision Making
Decisions made by machine learning models may have lasting impacts over ...
research
∙
07/22/2023
Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations
Robust reinforcement learning (RL) seeks to train policies that can perf...
research
∙
07/16/2023
Is Imitation All You Need? Generalized Decision-Making with Dual-Phase Training
We introduce DualMind, a generalist agent designed to tackle various dec...
research
∙
06/22/2023
TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning
Despite recent progress in reinforcement learning (RL) from raw pixel da...
research
∙
06/13/2023
A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning
Offline reinforcement learning (RL) provides a promising solution to lea...
research
∙
06/08/2023
Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning
Recent works have shown the potential of diffusion models in computer vi...
research
∙
05/27/2023
Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in Multi-Agent RL
Most existing works consider direct perturbations of victim's state/acti...
research
∙
02/06/2023
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness
The robustness of a deep classifier can be characterized by its margins:...
research
∙
01/24/2023
SMART: Self-supervised Multi-task pretrAining with contRol Transformers
Self-supervised pretraining has been extensively studied in language and...
research
∙
01/22/2023
Provable Unrestricted Adversarial Training without Compromise with Generalizability
Adversarial training (AT) is widely considered as the most promising str...
research
∙
11/02/2022
Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach
Data augmentation is a critical contributing factor to the success of de...
research
∙
10/14/2022
Distributional Reward Estimation for Effective Multi-Agent Deep Reinforcement Learning
Multi-agent reinforcement learning has drawn increasing attention in pra...
research
∙
10/12/2022
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning
Recent studies reveal that a well-trained deep reinforcement learning (R...
research
∙
06/21/2022
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems
Communication is important in many multi-agent reinforcement learning (M...
research
∙
01/01/2022
Transfer RL across Observation Feature Spaces via Model-Based Regularization
In many reinforcement learning (RL) applications, the observation space ...
research
∙
06/09/2021
Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL
Evaluating the worst-case performance of a reinforcement learning (RL) a...
research
∙
09/02/2020
Vulnerability-Aware Poisoning Mechanism for Online RL with Unknown Dynamics
Poisoning attacks, although have been studied extensively in supervised ...
research
∙
02/16/2020
TempLe: Learning Template of Transitions for Sample Efficient Multi-task RL
Transferring knowledge among various environments is important to effici...
research
∙
01/14/2020
Understanding Generalization in Deep Learning via Tensor Methods
Deep neural networks generalize well on unseen data though the number of...
research
∙
12/21/2019
