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<p>The Soft Actor-Critic (SAC) algorithm as a deep reinforcement learning (DRL) algorithm based on maximum entropy of off-policy. By combining off-policy with Actor-Critic and applying it to the autonomous navigation of mobile robots, SAC performs well in continuous state and action spaces. However, this maximum-entropy-based learning process often encounters instability issues. In this work, we introduced a multi-Q value strategy to enhance stability. Environmental drift is exacerbated by the interplay between inherent sensor errors and the constantly changing conditions of a dynamic environment, we emphasized the utilization of a neural plasticity learning mechanism to enhance model robustness. This mechanism selectively retains highly active neurons while pruning fewer active ones, ensuring dual-frequency updating and learning for both high- and low-activity neurons, thus improving the model’s adaptability to environmental changes. Through comparative experiments, we evaluated the performance of MQSF-AC against the SAC, TD3, DDPG, PPO and REDQ algorithms. The results demonstrate that MQSF-AC, our proposed algorithm integrating neural plasticity learning and multi-Q techniques, achieves favorable outcomes for path planning tasks in deep reinforcement learning.</p>

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MQSF-AC: Multi-Q with selective forgetting actor-critic for robot path planning

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Yuwan Gu,
  • Yan Chen,
  • Fang Meng,
  • Ronghai Miao,
  • Jie Hao,
  • Jidong Lv

摘要

The Soft Actor-Critic (SAC) algorithm as a deep reinforcement learning (DRL) algorithm based on maximum entropy of off-policy. By combining off-policy with Actor-Critic and applying it to the autonomous navigation of mobile robots, SAC performs well in continuous state and action spaces. However, this maximum-entropy-based learning process often encounters instability issues. In this work, we introduced a multi-Q value strategy to enhance stability. Environmental drift is exacerbated by the interplay between inherent sensor errors and the constantly changing conditions of a dynamic environment, we emphasized the utilization of a neural plasticity learning mechanism to enhance model robustness. This mechanism selectively retains highly active neurons while pruning fewer active ones, ensuring dual-frequency updating and learning for both high- and low-activity neurons, thus improving the model’s adaptability to environmental changes. Through comparative experiments, we evaluated the performance of MQSF-AC against the SAC, TD3, DDPG, PPO and REDQ algorithms. The results demonstrate that MQSF-AC, our proposed algorithm integrating neural plasticity learning and multi-Q techniques, achieves favorable outcomes for path planning tasks in deep reinforcement learning.