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Decision-making of Emergent Incident based on P-MADDPG [article]

Yibo Guo, Lishuo Hou, Mingxin Li, Yue Yuan, Shun Liu, Jingyi Xue, Yafang Han, Mingliang Xu
2022 arXiv   pre-print
In this paper, we propose a P-MADDPG algorithm to solve the emergency decision-making problem of emergent incidents, which predicts the nodes where an incident may occur in the next time by GRU model and  ...  A simulation environment was established for realistic scenarios, and three scenarios were selected to test the performance of P-MADDPG in emergency decision-making problems for emergent incidents: unmanned  ...  Multi-agent Emergency Decision-making Based on the above definition, the problem of multi-agent emergency decision-making is as follows: given an undirected graph G = (𝑉, 𝐸) to represent the operation  ... 
arXiv:2203.12673v1 fatcat:egz7rkw6y5f5vdoxx4oa6444ga

Multi Agent Reinforcement Learning Trajectory Design and Two-Stage Resource Management in CoMP UAV VLC Networks [article]

Mohammad Reza Maleki, Mohammad Robat Mili, Mohammad Reza Javan, Nader Mokari, Eduard A. Jorswieck
2021 arXiv   pre-print
The effect of accelerated motion in UAV is necessary to be considered. Unlike most existing works, we examine the effects of variable speed on kinetics and radio resource allocations.  ...  To handle this multiobjective optimization, we first apply the scalarization method and then apply multi-agent deep deterministic policy gradient (MADDPG).  ...  MEC-mounted macro eNodeBs (MeNBs) to allocate resources to vehicles and make association decisions in [19] .  ... 
arXiv:2111.05116v3 fatcat:dhnhnuxpafgclb6owqyp45b4ey

A Review of Cooperative Multi-Agent Deep Reinforcement Learning [article]

Afshin OroojlooyJadid, Davood Hajinezhad
2021 arXiv   pre-print
If applicable, we further make a connection among different papers in each category. Next, we cover some new emerging research areas in MARL along with the relevant recent papers.  ...  First, we elaborate on each of these methods, possible challenges, and how these challenges were mitigated in the relevant papers.  ...  As another extension of MADDPG, Chu and Ye (2017) consider multi-agent cooperative problems with N agents and proposes three actor-critic algorithms based on MADDPG.  ... 
arXiv:1908.03963v4 fatcat:s2umqzxmqrhntkev3f6k554cv4

Policy-focused Agent-based Modeling using RL Behavioral Models [article]

Osonde A. Osoba, Raffaele Vardavas, Justin Grana, Rushil Zutshi, Amber Jaycocks
2020 arXiv   pre-print
Agent-based Models (ABMs) are valuable tools for policy analysis. ABMs help analysts explore the emergent consequences of policy interventions in multi-agent decision-making settings.  ...  Standard specifications of agent behavioral models rely either on heuristic decision-making rules or on regressions trained on past data. Both prior specification modes have limitations.  ...  Agents using the default behavioral model continue to make decisions based on their adaptive strategy books. RL agents make decisions based on the outcome of their stochastic policy neural networks.  ... 
arXiv:2006.05048v3 fatcat:2xdlvcxtajfu5ekpj2zhqzmnai

Social physics [article]

Marko Jusup, Petter Holme, Kiyoshi Kanazawa, Misako Takayasu, Ivan Romic, Zhen Wang, Suncana Gecek, Tomislav Lipic, Boris Podobnik, Lin Wang, Wei Luo, Tin Klanjscek (+3 others)
2021 arXiv   pre-print
Based on the number of ideas laid out, but also on the fact that the field is already too big for an exhaustive review despite our best efforts, we are forced to conclude that the future for social physics  ...  Here, we dub the physics-inspired and physics-like work on societal problems "social physics", and pay our respect to intellectual mavericks who nurtured the field to its maturity.  ...  no decision-making value [318] .  ... 
arXiv:2110.01866v1 fatcat:ccfxyezl6zgddd6uvrxubmaxua