A Safety Modulator Actor-Critic Method in Model-Free Safe Reinforcement Learning and Application in UAV Hovering
<|reference_start|>A Safety Modulator Actor-Critic Method in Model-Free Safe Reinforcement Learning and Application in UAV Hovering: This paper proposes a safety modulator actor-critic (SMAC) method to address safety constraint and overestimation mitigation in model-free safe reinforcement learning (RL). A safety modulator is developed to satisfy safety constraints by modulating actions, allowing the policy to ignore safety constraint and focus on maximizing reward. Additionally, a distributional critic with a theoretical update rule for SMAC is proposed to mitigate the overestimation of Q-values with safety constraints. Both simulation and real-world scenarios experiments on Unmanned Aerial Vehicles (UAVs) hovering confirm that the SMAC can effectively maintain safety constraints and outperform mainstream baseline algorithms.<|reference_end|>
@article{qi2024a,
    title={A Safety Modulator Actor-Critic Method in Model-Free Safe Reinforcement
  Learning and Application in UAV Hovering},
    author={Qihan Qi, Xinsong Yang, Gang Xia, Daniel W. C. Ho, Pengyang Tang},
    journal={arXiv preprint arXiv:2410.06847},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06847},
    primaryClass={cs.AI cs.LG cs.RO}
}