Search Results for author:
Found 6 papers, 1 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.
Utilizing Mood-Inducing Background Music in Human-Robot Interaction
This research fills this gap by reporting the results of an experiment in which human participants were required to complete a task in the presence of an autonomous agent while listening to background music.
DM$^2$: Decentralized Multi-Agent Reinforcement Learning for Distribution Matching
The theoretical analysis shows that under certain conditions, each agent minimizing its individual distribution mismatch allows the convergence to the joint policy that generated the target distribution.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Artificial Musical Intelligence: A Survey
Computers have been used to analyze and create music since they were first introduced in the 1950s and 1960s.
Representative Selection in Non Metric Datasets
This paper considers the problem of representative selection: choosing a subset of data points from a dataset that best represents its overall set of elements.
DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation
In this work we present DJ-MC, a novel reinforcement-learning framework for music recommendation that does not recommend songs individually but rather song sequences, or playlists, based on a model of preferences for both songs and song transitions.
