Decentralized Best-Response-Based Learning in Two-Player Zero-Sum Stochastic Games: A Finite-Sample Analysis
arXiv:2409.01447v3 Announce Type: replace Abstract: We present a finite-sample analysis of decentralized learning in two-player zero-sum matrix games and stochastic games, with a focus on best-response-based learning algorithms. In matrix games, the learning algorithm is payoff-based and symmetric: each player updates its policy using only its own payoff observations, incrementally moving toward an estimated smoothed best response to the opponent's latest policy. For stochastic games, we build on this matrix-game primitive to develop a learning algorithm called value iteration with smoothed be
This paper represents continued progress in the theoretical underpinnings of decentralized AI agent learning, moving towards more robust and self-improving autonomous systems.
Advanced decentralized learning mechanisms are crucial for developing sophisticated AI agents, which are expected to automate complex tasks and workflows across various industries.
The ability of AI agents to learn and adapt effectively in multi-agent, competitive environments without central coordination is being refined, enhancing their potential for real-world deployment.
- · AI Agent Developers
- · Automation Sector
- · Research Institutions
Improved theoretical understanding and practical algorithms for decentralized AI agent learning are developed.
More robust and autonomous AI agents capable of operating in complex, competitive environments emerge.
These advanced agents accelerate automation across industries, potentially impacting white-collar employment and the structure of work.
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