Search Results for author:
Found 10 papers, 2 papers with code
Replication of Impedance Identification Experiments on a Reinforcement-Learning-Controlled Digital Twin of Human Elbows
We compared the elbow movement controlled by an RL agent with the motion of an actual human elbow in terms of the impedance identified in torque-perturbation experiments.
Enhancing AUV Autonomy With Model Predictive Path Integral Control
However, in order to ensure that the AUV is able to carry out its mission successfully, a control system capable of adapting to changing environmental conditions is required.
Learning Complex Teamwork Tasks Using a Given Sub-task Decomposition
Training a team to complete a complex task via multi-agent reinforcement learning can be difficult due to challenges such as policy search in a large joint policy space, and non-stationarity caused by mutually adapting agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning
These belief estimates are combined with our solution for the fully observable case to compute an agent's optimal policy under partial observability in open ad hoc teamwork.
Deep Reinforcement Learning for Multi-Agent Interaction
The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning.
Generating Teammates for Training Robust Ad Hoc Teamwork Agents via Best-Response Diversity
Early approaches address the AHT challenge by training the learner with a diverse set of handcrafted teammate policies, usually designed based on an expert's domain knowledge about the policies the learner may encounter.
Few-Shot Teamwork
We propose the novel few-shot teamwork (FST) problem, where skilled agents trained in a team to complete one task are combined with skilled agents from different tasks, and together must learn to adapt to an unseen but related task.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A Survey of Ad Hoc Teamwork Research
Ad hoc teamwork is the research problem of designing agents that can collaborate with new teammates without prior coordination.
Data-driven controllers and the need for perception systems in underwater manipulation
In this article, we introduce a control strategy for UVMSs working with unknown payloads.
Marine Vehicles Localization Using Grid Cells for Path Integration
In this article, we show how grid cells can be used for obtaining a position estimation of underwater vehicles.
