103 citations · 355 across the 24 of their papers we have counts for
6 papers · 1 filter
The Eigenoption-Critic Framework
Miao Liu, Marlos C. Machado, Gerald Tesauro +1
Eigenoptions (EOs) have been recently introduced as a promising idea for generating a diverse set of options through the graph Laplacian, having been shown to allow efficient explo…
Eigenoption Discovery through the Deep Successor Representation
Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo +3
Options in reinforcement learning allow agents to hierarchically decompose a task into subtasks, having the potential to speed up learning and planning. However, autonomously learn…
Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions
Miao Liu, Kavinayan Sivakumar, Shayegan Omidshafiei +2
This paper presents a data-driven approach for multi-robot coordination in partially-observable domains based on Decentralized Partially Observable Markov Decision Processes (Dec-P…
Scalable Accelerated Decentralized Multi-Robot Policy Search in Continuous Observation Spaces
Shayegan Omidshafiei, Christopher Amato, Miao Liu +3
This paper presents the first ever approach for solving \emph{continuous-observation} Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) and their semi-Marko…
Semantic-level Decentralized Multi-Robot Decision-Making using Probabilistic Macro-Observations
Shayegan Omidshafiei, Shih-Yuan Liu, Michael Everett +5
Robust environment perception is essential for decision-making on robots operating in complex domains. Intelligent task execution requires principled treatment of uncertainty sourc…
Socially Aware Motion Planning with Deep Reinforcement Learning
Yu Fan Chen, Michael Everett, Miao Liu +1
For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the…