Critic-over-Actor-Critic Modeling: Finding Optimal Strategy in ICU Environments

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.