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Found 20 papers, 8 papers with code
Creative Robot Tool Use with Large Language Models
This paper investigates the feasibility of imbuing robots with the ability to creatively use tools in tasks that involve implicit physical constraints and long-term planning.
Hyper-Decision Transformer for Efficient Online Policy Adaptation
To address this challenge, we propose a new framework, called Hyper-Decision Transformer (HDT), that can generalize to novel tasks from a handful of demonstrations in a data- and parameter-efficient manner.
Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?
The learned embeddings are evaluated on two downstream tasks: (1) automatic ECG diagnosis report generation, and (2) zero-shot cardiovascular disease detection.
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators.
Continual Vision-based Reinforcement Learning with Group Symmetries
Continual reinforcement learning aims to sequentially learn a variety of tasks, retaining the ability to perform previously encountered tasks while simultaneously developing new policies for novel tasks.
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation
Curriculum Reinforcement Learning (CRL) aims to create a sequence of tasks, starting from easy ones and gradually learning towards difficult tasks.
Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment
Multimedia summarization with multimodal output (MSMO) is a recently explored application in language grounding.
Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability
A trustworthy reinforcement learning algorithm should be competent in solving challenging real-world problems, including {robustly} handling uncertainties, satisfying {safety} constraints to avoid catastrophic failures, and {generalizing} to unseen scenarios during deployments.
Can Brain Signals Reveal Inner Alignment with Human Languages?
In addition, we provide interpretations of the performance improvement: (1) feature distribution shows the effectiveness of the alignment module for discovering and encoding the relationship between EEG and language; (2) alignment weights show the influence of different language semantics as well as EEG frequency features; (3) brain topographical maps provide an intuitive demonstration of the connectivity in the brain regions.
Prompting Decision Transformer for Few-Shot Policy Generalization
Humans can leverage prior experience and learn novel tasks from a handful of demonstrations.
MHMS: Multimodal Hierarchical Multimedia Summarization
Multimedia summarization with multimodal output can play an essential role in real-world applications, i. e., automatically generating cover images and titles for news articles or providing introductions to online videos.
Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training
In this paper, we introduce a novel hierarchical formulation of robust RL - a general-sum Stackelberg game model called RRL-Stack - to formalize the sequential nature and provide extra flexibility for robust training.
Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation
In this paper, we focus on a new method of data augmentation to solve the data imbalance problem within imbalanced ECG datasets to improve the robustness and accuracy of heart disease detection.
Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling
Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces where limited scalability dictates a prohibitively large number of testing iterations.
Functional optimal transport: map estimation and domain adaptation for functional data
We introduce a formulation of optimal transport problem for distributions on function spaces, where the stochastic map between functional domains can be partially represented in terms of an (infinite-dimensional) Hilbert-Schmidt operator mapping a Hilbert space of functions to another.
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments
The algorithm is evaluated in realistic safety-critical environments with non-stationary disturbances.
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
We propose a transition prior to account for the temporal dependencies in streaming data and update the mixture online via sequential variational inference.
Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environments
We also test the proposed algorithm in traffic scenarios that require coordination of all autonomous vehicles to show the practical value of delay-awareness.
Delay-Aware Model-Based Reinforcement Learning for Continuous Control
Action delays degrade the performance of reinforcement learning in many real-world systems.
CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios
However, most of the data is collected in safe scenarios leading to the duplication of trajectories which are easy to be handled by currently developed algorithms.
