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Optimistic sequential multi-agent reinforcement learning with motivational communication.

Anqi Huang1, Yongli Wang1, Xiaoliang Zhou1

  • 1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 28, 2024
PubMed
Summary
This summary is machine-generated.

Optimistic Sequential Soft Actor Critic with Motivational Communication (OSSMC) enhances multi-agent reinforcement learning by exploring optimistic Q-values and using motivational messages. This novel method achieves faster convergence and superior performance in cooperative tasks.

Keywords:
Motivational communicationMulti-agent reinforcement learningMulti-agent systemPolicy gradientReinforcement learning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Multi-Agent Systems

Background:

  • Centralized Training with Decentralized Execution (CTDE) is common in Multi-Agent Reinforcement Learning (MARL).
  • Existing algorithms suffer from underestimation of action values, leading to sub-optimal solutions.
  • Complex communication methods hinder focus on essential information.

Purpose of the Study:

  • To introduce a novel method, Optimistic Sequential Soft Actor Critic with Motivational Communication (OSSMC).
  • To address sub-optimal convergence and communication complexity in CTDE MARL algorithms.

Main Methods:

  • OSSMC utilizes optimistic Q-values for greedy exploration of individual policy potential.
  • A sequential update mechanism with optimistic Q-values ensures monotonic joint policy improvement.
  • Motivational communication modules foster cooperative behaviors.
  • Soft Actor Critic (SAC) value regularization enhances entropy and exploration.

Main Results:

  • OSSMC demonstrated superior performance compared to baseline algorithms on benchmark datasets.
  • The proposed method exhibited a significantly faster convergence rate.
  • Empirical results validate the effectiveness of optimistic Q-values and motivational communication.

Conclusions:

  • OSSMC effectively overcomes limitations of existing CTDE MARL algorithms.
  • The method promotes faster learning and better cooperative strategies.
  • OSSMC represents a promising advancement in fully cooperative MARL.