Search Results for author:
Found 11 papers, 4 papers with code
N-Agent Ad Hoc Teamwork
POAM is a policy gradient, multi-agent reinforcement learning approach to the NAHT problem, that enables adaptation to diverse teammate behaviors by learning representations of teammate behaviors.
Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents
In this work, we first propose that maximizing an AHT agent's robustness requires it to emulate policies in the minimum coverage set (MCS), the set of best-response policies to any partner policies in the environment.
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.
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
Sharing parameters in multi-agent deep reinforcement learning has played an essential role in allowing algorithms to scale to a large number of agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning
Ad hoc teamwork is the challenging problem of designing an autonomous agent which can adapt quickly to collaborate with teammates without prior coordination mechanisms, including joint training.
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning
Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains.
