The Convergence of a Cooperation Markov Decision Process System

Area of Science:

• Artificial Intelligence
• Reinforcement Learning
• Game Theory

Background:

• Traditional Markov Decision Processes focus on single agents.
• Multi-agent systems are increasingly prevalent in complex applications.
• Cooperative decision-making presents unique challenges in agent interactions.

Purpose of the Study:

• Introduce a Cooperation Markov Decision Process (CMDP) for two-agent systems.
• Analyze the convergence properties of the value function in CMDPs.
• Develop an algorithm to find optimal cooperative strategies.

Main Methods:

• Formulation of a two-agent Cooperation Markov Decision Process.
• Theoretical analysis of value function convergence.
• Development of an algorithm for optimal strategy pair identification.

Main Results:

• The value function in the CMDP system converges.
• Convergence value is independent of the initial value function.
• An algorithm successfully identifies optimal strategy pairs.

Conclusions:

• The CMDP framework effectively models cooperative decision-making between two agents.
• The convergence properties ensure stable learning outcomes.
• The proposed algorithm provides a method for achieving optimal cooperation.