# Decision-Time Learning and Planning Integrated Control for the Mild Hyperbaric Chamber
## 1 Introduction
[PERSON] et al. proposed a hypoxic chamber control system based on a fuzzy adaptive control algorithm, which  
Figure 1: Vehicle-mounted plateau MHBC. integrates expert and proportional-derivative control strategies to effectively address time delay and uncertainty in the chamber environment [4]. [PERSON] et al. achieved the automation of a multi-place hyperbaric chamber using a zero-pole cancellation regulator, ensuring accurate pressure tracking and repeatability, and successfully deployed the system in a clinical setting [5]. [PERSON] et al. developed an adaptive PID controller based on artificial intelligence for pressure regulation in surgical hyperbaric chambers, significantly improving system responsiveness, although challenges such as pressure overshoot and disturbance rejection remain unresolved [6]. Most existing studies adopt PID or fuzzy control strategies, which generally offer limited control precision and inadequate disturbance rejection capabilities. Furthermore, these approaches often lack a systematic consideration of the various factors influencing pressure variation. With the advent of the era of artificial intelligence, medical devices are increasingly evolving toward intelligent and user-centric systems. In the context of hyperbaric chamber usage, users are becoming increasingly concerned not only with treatment efficacy but also with the comfort and convenience of the therapeutic experience. Over the past decade, machine learning and deep learning-based technologies have rapidly developed, playing an increasingly significant role in daily life. As a subfield of machine learning, Reinforcement learning (RL) has gained increasing attention due to its ability to handle sequential decision-making problems [7]. RL represents the third category of machine learning, alongside supervised learning and unsupervised learning. Since the 1980s, classical RL methods have been continuously developed. With advancements in deep learning, RL has entered a phase of accelerated growth. In 2016, Google DeepMind leveraged Monte Carlo Tree Search (MCTS) to develop AlphaGo, which ultimately defeated the reigning world champion in Go, generating widespread interest [8]. By 2023, RL had been extensively applied in training large language models such as ChatGPT, where Reinforcement Learning from Human Feedback (RLHF) was used to fine-tune responses based on human evaluations [9]. Currently, RL is widely applied in robotics, game intelligence, smart cities, recommendation systems, and energy optimization, demonstrating immense potential. In recent years, RL has attracted growing interest in process control applications, primarily due to its capability to handle high-dimensional nonlinear systems, time-varying environments, and constraint optimization problems. By interacting with the environment to learn optimal policies, RL enables intelligent agents to maximize cumulative rewards in multi-step decision-making processes. This provides an intelligent control approach that overcomes the limitations of traditional control methods. However, the primary challenges of applying reinforcement learning methods to the control of MHBC include data efficiency and non-stationary dynamics. On one hand, reinforcement learning methods rely on extensive interaction data and an iterative trial-and-error mechanism to optimize policies. In the control scenario of MHBC, frequent experimental interactions may cause damage to critical components such as control valves. Additionally, due to the high-pressure and enclosed nature of the MHBC environment, entering an abnormal state during the trial-and-error process could pose serious threats to system safety. On the other hand, during long-time operation, MHBC is prone to abnormal conditions such as valve failure. If the controller fails to adjust the control in time, the system pressure may deviate from normal range, jeopardizing the stability and safety of the system.