Deep Reinforcement Learning in Nonstationary Environments With Unknown Change Points.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Deep reinforcement learning (DRL) excels in stationary environments.
• Nonstationary environments, common in real-world applications like robotics and recommendations, pose significant challenges to DRL stability and robustness due to changing dynamics.
• Existing solutions often require prior knowledge of environmental change points, which is frequently unavailable.

Purpose of the Study:

• To develop a robust DRL algorithm capable of operating effectively in nonstationary environments without prior knowledge of change points.
• To enable DRL agents to adapt rapidly and stably to dynamic environmental shifts.

Main Methods:

• A novel DRL algorithm that actively detects environmental change points by monitoring the joint distribution of states and actions.
• Implementation of a detection-boosted, gradient-constrained optimization technique.
• Leveraging previously trained policies and experiences to accelerate adaptation to new environmental conditions.

Main Results:

• The proposed algorithm achieved the highest cumulative reward compared to several alternative methods.
• The method demonstrated the fastest adaptation speed when transitioning to new environments.
• Experimental validation confirmed the algorithm's effectiveness in handling unknown environmental changes.

Conclusions:

• The developed DRL algorithm offers a robust solution for nonstationary environments with unknown change points.
• This approach significantly enhances the environmental suitability and adaptability of intelligent agents like drones, autonomous vehicles, and underwater robots.