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Found 6 papers, 2 papers with code
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
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.
Towards Quantum-Secure Authentication and Key Agreement via Abstract Multi-Agent Interaction
Authentication and key agreement are decided based on the agents' observed behaviors during the interaction.
