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RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
[article]
2021
arXiv
pre-print
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To address these issues, we propose RMIX, a novel cooperative MARL method with the Conditional Value at Risk (CVaR) measure over the learned distributions of individuals' Q values. ...
Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE). ...
Conclusion and Future Work In this paper, we propose RMIX, a novel and practical MARL method with CVaR over the learned distributions of individuals' Q values as risk-sensitive policies for cooperative ...
arXiv:2102.08159v3
fatcat:ccp4foqc6zfdrae2pfltaykd3y
Learning Generalizable Risk-Sensitive Policies to Coordinate in Decentralized Multi-Agent General-Sum Games
[article]
2022
arXiv
pre-print
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While various multi-agent reinforcement learning methods have been proposed in cooperative settings, few works investigate how self-interested learning agents achieve mutual coordination in decentralized ...
general-sum games and generalize pre-trained policies to non-cooperative opponents during execution. ...
[43] propose a decentralized risk-sensitive policy LH-IQN for all agents to seek higher team rewards. ...
arXiv:2205.15859v1
fatcat:ev6fyiei3vh6pd2yve22idwepa
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning
2022
International Conference on Machine Learning
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To this end, we propose Disentangled RIsk-sensitive Multi-Agent reinforcement learning (DRIMA) to separately access the risk sources. ...
In cooperative multi-agent reinforcement learning, the outcomes of agent-wise policies are highly stochastic due to the two sources of risk: (a) random actions taken by teammates and (b) random transition ...
For such environments in single-agent settings, risk-sensitive reinforcement learning (RL) (Chow & Ghavamzadeh, 2014) has shown remarkable results by using policies that consider risk rather than simple ...
dblp:conf/icml/SonKARYS22
fatcat:6h3din6thfbbzdzy3gstrmzk5u
Off-Beat Multi-Agent Reinforcement Learning
[article]
2022
arXiv
pre-print
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We investigate model-free multi-agent reinforcement learning (MARL) in environments where off-beat actions are prevalent, i.e., all actions have pre-set execution durations. ...
It boosts multi-agent learning by addressing the challenging temporal credit assignment problem raised by the off-beat actions via our novel reward redistribution scheme, alleviating the issue of non-Markovian ...
It replaces the Q value policy with CVaR [43] for risk-sensitive policy learning. ...
arXiv:2205.13718v2
fatcat:3jpom4oiazeldj7ogbp4oi5zji
The Russian Academy of Sciences, 2006 Update
[article]
2020
Preserved Fulltext
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The scientific, educational and organizing activities of the great scientist and a person of encyclopaedic learning. Mikhail Lomonosov made as much as a whole era in the Academy's history. ...
The press was assigned to publish all kinds (except for ecclesiastical) of the literature in the country. ...
Along with the models for evaluating the radiation risk, IBRAE, in cooperation with the IRFChP of the National Academy of Sciences of Belarus, develops computer codes for risk analysis related with chemically ...
doi:10.26153/tsw/10405
fatcat:tqi3ovf3uvefja5jxzi3e7x7ea