Search Results for author:
Found 11 papers, 5 papers with code
StarCraftImage: A Dataset For Prototyping Spatial Reasoning Methods For Multi-Agent Environments
Spatial reasoning tasks in multi-agent environments such as event prediction, agent type identification, or missing data imputation are important for multiple applications (e. g., autonomous surveillance over sensor networks and subtasks for reinforcement learning (RL)).
DisasterResponseGPT: Large Language Models for Accelerated Plan of Action Development in Disaster Response Scenarios
The development of plans of action in disaster response scenarios is a time-consuming process.
Imitation Learning with Human Eye Gaze via Multi-Objective Prediction
In this work, we propose Gaze Regularized Imitation Learning (GRIL), a novel context-aware, imitation learning architecture that learns concurrently from both human demonstrations and eye gaze to solve tasks where visual attention provides important context.
PODNet: A Neural Network for Discovery of Plannable Options
Learning from demonstration has been widely studied in machine learning but becomes challenging when the demonstrated trajectories are unstructured and follow different objectives.
Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments
However, it is currently unclear how to efficiently update that policy using reinforcement learning as these approaches are inherently optimizing different objective functions.
On Memory Mechanism in Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) extends (single-agent) reinforcement learning (RL) by introducing additional agents and (potentially) partial observability of the environment.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time
This paper investigates how to utilize different forms of human interaction to safely train autonomous systems in real-time by learning from both human demonstrations and interventions.
Coordination-driven learning in multi-agent problem spaces
We discuss the role of coordination as a direct learning objective in multi-agent reinforcement learning (MARL) domains.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Cycle-of-Learning for Autonomous Systems from Human Interaction
We discuss different types of human-robot interaction paradigms in the context of training end-to-end reinforcement learning algorithms.
Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials
Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli.
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI.
