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Learning Automata-Based Multiagent Reinforcement Learning for Optimization of Cooperative Tasks.

Zhen Zhang, Dongqing Wang, Junwei Gao

IEEE Transactions on Neural Networks and Learning Systems

|October 7, 2020

Summary

This study introduces the Learning Automata for Optimization of Cooperative Agents (LA-OCA) algorithm, enhancing multiagent reinforcement learning for cooperative tasks. LA-OCA achieves a 100% success rate in finding optimal joint strategies, outperforming existing methods in learning speed.

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Area of Science:

Artificial Intelligence
Machine Learning
Game Theory

Background:

Multiagent reinforcement learning (MARL) is widely applied due to its implementation simplicity and task distribution capabilities.
Learning automata, a type of MARL, are effective for finding optimal joint actions or equilibria without requiring agents to observe each other.
Existing learning automata algorithms have limited application in cooperative repeated and stochastic games.

Purpose of the Study:

To propose a novel algorithm, Learning Automata for Optimization of Cooperative Agents (LA-OCA), for optimizing cooperative tasks within MARL.
To adapt learning automata for cooperative scenarios by transforming the environment into a P-model using an indicator variable.

Main Methods:

The proposed LA-OCA algorithm transforms the cooperative task environment into a P-model.
An indicator variable is introduced to signify maximal reward acquisition, facilitating the application of learning automata to cooperative settings.
Theoretical analysis is conducted to validate the stability of optimal joint actions as critical points within the LA-OCA model.

Main Results:

Theoretical analysis confirms that strict optimal joint actions are stable critical points for LA-OCA in cooperative repeated games.
Simulation results demonstrate LA-OCA's 100% success rate in identifying pure optimal joint strategies across three cooperative tasks.
LA-OCA exhibits superior performance compared to other algorithms, particularly in terms of learning speed.

Conclusions:

LA-OCA is a highly effective algorithm for optimizing cooperative agents in multiagent reinforcement learning scenarios.
The P-model transformation enables learning automata to successfully address cooperative tasks, achieving optimal outcomes.
LA-OCA offers a robust and efficient solution for cooperative game optimization, surpassing existing methods.