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Meta attention for Off-Policy Actor-Critic.

Jiateng Huang1, Wanrong Huang1, Long Lan1

  • 1National University of Defense Technology, College of Computer Science and Technology, Institute for Quantum Information & State Key Laboratory of High Performance Computing, Changsha, 410073, Hunan, China.

Neural Networks : the Official Journal of the International Neural Network Society
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Summary

This study introduces a novel meta-attention method for reinforcement learning, enhancing off-policy actor-critic algorithms. The approach improves performance in continuous control tasks by integrating attention and meta-learning.

Keywords:
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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Reinforcement Learning

Background:

  • Off-policy actor-critic methods leverage past experiences for success in reinforcement learning.
  • Attention mechanisms improve sampling efficiency in image-based and multi-agent reinforcement learning tasks.

Purpose of the Study:

  • To propose a meta-attention method for state-based reinforcement learning tasks.
  • To combine attention mechanisms and meta-learning within the off-policy actor-critic framework.

Main Methods:

  • Introduced attention in both the Actor and Critic components of the actor-critic framework.
  • Applied the meta-attention approach to state-based reinforcement learning, differentiating from pixel or information-source attention.
  • Ensured the meta-attention method functions during both gradient-based training and agent decision-making.

Main Results:

  • Demonstrated the superiority of the meta-attention method in various continuous control tasks.
  • Validated the effectiveness of the proposed method within off-policy actor-critic frameworks like DDPG and TD3.

Conclusions:

  • The proposed meta-attention method offers significant improvements for off-policy actor-critic reinforcement learning.
  • This approach enhances performance in continuous control tasks by integrating meta-learning and attention mechanisms effectively.