  Reinforcement learning has been increasingly applied in monitoring
applications because of its ability to learn from previous experiences and can
make adaptive decisions. However, existing machine learning-based health
monitoring applications are mostly supervised learning algorithms, trained on
labels and they cannot make adaptive decisions in an uncertain complex
environment. This study proposes a novel and generic system, predictive deep
reinforcement learning (PDRL) with multiple RL agents in a time series
forecasting environment. The proposed generic framework accommodates virtual
Deep Q Network (DQN) agents to monitor predicted future states of a complex
environment with a well-defined reward policy so that the agent learns existing
knowledge while maximizing their rewards. In the evaluation process of the
proposed framework, three DRL agents were deployed to monitor a subject's
future heart rate, respiration, and temperature predicted using a BiLSTM model.
With each iteration, the three agents were able to learn the associated
patterns and their cumulative rewards gradually increased. It outperformed the
baseline models for all three monitoring agents. The proposed PDRL framework is
able to achieve state-of-the-art performance in the time series forecasting
process. The proposed DRL agents and deep learning model in the PDRL framework
are customized to implement the transfer learning in other forecasting
applications like traffic and weather and monitor their states. The PDRL
framework is able to learn the future states of the traffic and weather
forecasting and the cumulative rewards are gradually increasing over each
episode.
