scholarly journals Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games

2022 ◽  
Author(s):  
Weichao Mao ◽  
Tamer Başar
Keyword(s):  
Reinforcement Learning ◽  
Markov Games

Solving Cyber Alert Allocation Markov Games with Deep Reinforcement Learning

2019 ◽  
pp. 164-183 ◽  
Author(s):  
Noah Dunstatter ◽  
Alireza Tahsini ◽  
Mina Guirguis ◽  
Jelena Tešić
Keyword(s):  
Reinforcement Learning ◽  
Markov Games

Multi-agent reinforcement learning using ordinal action selection and approximate policy iteration

2016 ◽  
Vol 14 (06) ◽  
pp. 1650053
Author(s):  
Daxue Liu ◽  
Jun Wu ◽  
Xin Xu
Keyword(s):  
Reinforcement Learning ◽  
Single Agent ◽  
Action Selection ◽  
Policy Iteration ◽  
Common Interest ◽  
Policy Space ◽  
Markov Games ◽  
Approximate Policy Iteration ◽  
Multi Agent ◽  
Agent Coordination

Multi-agent reinforcement learning (MARL) provides a useful and flexible framework for multi-agent coordination in uncertain dynamic environments. However, the generalization ability and scalability of algorithms to large problem sizes, already problematic in single-agent RL, is an even more formidable obstacle in MARL applications. In this paper, a new MARL method based on ordinal action selection and approximate policy iteration called OAPI (Ordinal Approximate Policy Iteration), is presented to address the scalability issue of MARL algorithms in common-interest Markov Games. In OAPI, an ordinal action selection and learning strategy is integrated with distributed approximate policy iteration not only to simplify the policy space and eliminate the conflicts in multi-agent coordination, but also to realize the approximation of near-optimal policies for Markov Games with large state spaces. Based on the simplified policy space using ordinal action selection, the OAPI algorithm implements distributed approximate policy iteration utilizing online least-squares policy iteration (LSPI). This resulted in multi-agent coordination with good convergence properties with reduced computational complexity. The simulation results of a coordinated multi-robot navigation task illustrate the feasibility and effectiveness of the proposed approach.

scholarly journals Network Selection in 5G Networks Based on Markov Games and Friend-or-Foe Reinforcement Learning

2020 ◽  
Author(s):  
Alessandro Giuseppi ◽  
Emanuele De Santis ◽  
Francesco Delli Priscoli ◽  
Seok Ho Won ◽  
Taesang Choi ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Network Selection ◽  
5G Networks ◽  
Markov Games

A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms

1999 ◽  
Vol 11 (8) ◽  
pp. 2017-2060 ◽  
Author(s):  
Csaba Szepesvári ◽  
Michael L. Littman
Keyword(s):  
Reinforcement Learning ◽  
Value Function ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Sequential Decision ◽  
Q Learning ◽  
Markov Games ◽  
Optimal Behavior ◽  
Risk Sensitive ◽  
Optimal Value

Reinforcement learning is the problem of generating optimal behavior in a sequential decision-making environment given the opportunity of interacting with it. Many algorithms for solving reinforcement-learning problems work by computing improved estimates of the optimal value function. We extend prior analyses of reinforcement-learning algorithms and present a powerful new theorem that can provide a unified analysis of such value-function-based reinforcement-learning algorithms. The usefulness of the theorem lies in how it allows the convergence of a complex asynchronous reinforcement-learning algorithm to be proved by verifying that a simpler synchronous algorithm converges. We illustrate the application of the theorem by analyzing the convergence of Q-learning, model-based reinforcement learning, Q-learning with multistate updates, Q-learning for Markov games, and risk-sensitive reinforcement learning.

, a simple reinforcement learning scheme for two-player zero-sum Markov games

2009 ◽  
Vol 72 (7-9) ◽  
pp. 1494-1507
Author(s):  
Benoît Frénay ◽  
Marco Saerens
Keyword(s):  
Reinforcement Learning ◽  
Markov Games ◽  
Learning Scheme ◽  
Zero Sum
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