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An off-policy multi-agent stochastic policy gradient algorithm for cooperative continuous control.

Delin Guo1, Lan Tang1, Xinggan Zhang1

  • 1School of Electronic Science and Engineering, Nanjing University, Nanjing, 210093, China.

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|December 6, 2023
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Summary
This summary is machine-generated.

This study introduces an off-policy algorithm to improve data efficiency in multi-agent reinforcement learning (MARL) using trust regions. The new method enhances learning performance by utilizing historical data, outperforming existing approaches.

Keywords:
Deep reinforcement learning (DRL)Multi-agent MuJoCoMulti-agent controlMulti-agent reinforcement learning (MARL)Trust region

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Robotics

Background:

  • Trust region (TR) methods are successful in cooperative multi-agent reinforcement learning (MARL).
  • Existing TR-based MARL algorithms are primarily on-policy, leading to low sample efficiency due to inability to use historical data.
  • This limits the practical application of TR-based MARL in complex, data-intensive scenarios.

Purpose of the Study:

  • To enhance the data efficiency of trust region-based multi-agent reinforcement learning methods.
  • To develop an off-policy algorithm that can leverage historical data for improved learning.
  • To ensure monotonic policy improvement while using historical data within trust region constraints.

Main Methods:

  • Designed an approximation of the original objective function for off-policy optimization.
  • Proved monotonic improvement of the original objective by optimizing the approximated function with historical data under KL divergence constraints.
  • Proposed a practical off-policy multi-agent stochastic policy gradient algorithm within the centralized training with decentralized execution (CTDE) framework.
  • Integrated policy entropy into the reward to encourage exploration and enhance stability.

Main Results:

  • The proposed off-policy algorithm significantly outperforms existing algorithms on the multi-agent MuJoCo (MAMuJoCo) benchmark.
  • Demonstrated effective utilization of historical data for improved sample efficiency in cooperative continuous multi-agent control tasks.
  • The algorithm achieved superior performance across a range of challenging cooperative tasks.

Conclusions:

  • The developed off-policy TR-based MARL algorithm offers a substantial improvement in data efficiency and performance.
  • The method provides a viable solution for leveraging historical data in MARL, overcoming limitations of on-policy approaches.
  • This work advances the state-of-the-art in cooperative multi-agent continuous control.