Deep hierarchical reinforcement learning to manage the trade-off between sustainability and profitability in common pool resources systems

Author(s):  
Poiron-Guidoni Nicolas ◽  
Bisgambiglia Paul-Antoine
2021 ◽  
Vol 145 ◽  
pp. 105516
Author(s):  
Matthew Lorenzen ◽  
Quetzalcóatl Orozco-Ramírez ◽  
Rosario Ramírez-Santiago ◽  
Gustavo G. Garza

2021 ◽  
Vol 54 (5) ◽  
pp. 1-35
Author(s):  
Shubham Pateria ◽  
Budhitama Subagdja ◽  
Ah-hwee Tan ◽  
Chai Quek

Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of challenging long-horizon decision-making tasks into simpler subtasks. During the past years, the landscape of HRL research has grown profoundly, resulting in copious approaches. A comprehensive overview of this vast landscape is necessary to study HRL in an organized manner. We provide a survey of the diverse HRL approaches concerning the challenges of learning hierarchical policies, subtask discovery, transfer learning, and multi-agent learning using HRL. The survey is presented according to a novel taxonomy of the approaches. Based on the survey, a set of important open problems is proposed to motivate the future research in HRL. Furthermore, we outline a few suitable task domains for evaluating the HRL approaches and a few interesting examples of the practical applications of HRL in the Supplementary Material.


2013 ◽  
Vol 9 (4) ◽  
pp. 381-385 ◽  
Author(s):  
GEOFFREY M. HODGSON

Abstract:This introduction considers the overall character and impact of the work of Elinor Ostrom (1933–2012). Her work is not only inter-disciplinary in character; it also bridges ‘original’ and ‘new’ traditions within institutional economics. Her studies of the governance of common-pool resources inspired multiple lines of enquiry in economics and other social sciences. It also carves out a policy approach that surpasses the market–state dichotomy. This broad impact is evidenced in the seven essays collected and introduced here.


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