# PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring

arXiv: https://arxiv.org/abs/2309.10576 · HTML: https://mlsanity.com/paper/2309.10576/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Reinforcement Learning in Healthcare: A Survey](https://mlsanity.com/paper/1908.08796/) (2019)
- [Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2106.12931/) (2021)
- [Behavior Priors for Efficient Reinforcement Learning](https://mlsanity.com/paper/2010.14274/) (2020)
- [A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patients](https://mlsanity.com/paper/1605.04070/) (2016)
- [Probabilistic Machine Learning for Healthcare](https://mlsanity.com/paper/2009.11087/) (2020)
- [AI enabled RPM for Mental Health Facility](https://mlsanity.com/paper/2301.08828/) (2023)
- [Data mining and analysis of scientific research data records on Covid 19 mortality, immunity, and vaccine development in the first wave of the Covid 19 pandemic](https://mlsanity.com/paper/2009.05793/) (2020)

## Abstract (arXiv)

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.