PDRL: Multi-agent Based Reinforcement Learning For Predictive Monitoring

Abstract

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, t

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyshaik2023pdrl

Related papers

• Adaptive Multi-agent Deep Reinforcement Learning For Timely Healthcare Interventions (2023)0.00
• Reinforcement Learning In Dynamic Treatment Regimes Needs Critical Reexamination (2024)2.35
• Cooperative Informative Sensing For Monitoring Dynamic Indoor Environments Via Multi-agent Reinforcement Learning (2026)0.00
• Deep Reinforcement Learning For Multi-agent Systems: A Review Of Challenges, Solutions And Applications (2018)22.57
• Deep Multi-agent Reinforcement Learning With Discrete-continuous Hybrid Action Spaces (2019)12.47
• Real-time Recurrent Reinforcement Learning (2023)2.26
• A Survey And Critique Of Multiagent Deep Reinforcement Learning (2018)20.07
• Multi-agent Reinforcement Learning: A Report On Challenges And Approaches (2018)0.00