Critic-over-Actor-Critic Modeling: Finding Optimal Strategy in ICU Environments
Critic-over-Actor-Critic Modeling: Finding Optimal Strategy in ICU Environments
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DOI：
10.1109/bigdata55660.2022.10021125
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Riazat Ryan;Ming Shao
中科院分区：
文献类型：
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作者：
Riazat Ryan;Ming Shao
强化学习（RL）是机械化的，从经验中学习。它通过在环境中对不同行为的实验来优化奖惩，从而解决了顺序决策中的问题。与监督学习模型不同，RL缺乏静态输入输出映射和向量误差最小化的目标。然而，为了找到最优策略，关键是要学习来自训练数据的连续反馈和经验的离线规则，而不显式依赖于在线样本。在本文中，我们提出了一个多智能体强化学习框架，其中包括在半离线模式的批评家在一个在线的Actor-Critic网络，即，Critic-over-Actor-Critic（CoAC）模型，在寻找ICU患者的最佳治疗计划，以及在一个战斗游戏中的最佳策略进行批评。为了进一步验证，我们还检查了对抗分配中的模型。
Reinforcement learning (RL) is mechanized to learn from experience. It solves the problem in sequential decisions by optimizing reward-punishment through experimentation of the distinct actions in an environment. Unlike supervised learning models, RL lacks static input-output mappings and the objective of minimization of a vector error. However, to find out an optimal strategy, it is crucial to learn both continuous feedback from training data and the offline rules of the experiences with no explicit dependence on online samples. In this paper, we present a study of a multi-agent RL framework which involves a Critic in semi-offline mode criticizing over an online Actor-Critic network, namely, Critic-over-Actor-Critic (CoAC) model, in finding optimal treatment plan of ICU patients as well as optimal strategy in a combative battle game. For further validation, we also examine the model in the adversarial assignment.