A delay-robust method for enhanced real-time reinforcement learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Traditional reinforcement learning (RL) uses a blocking paradigm (Markov Decision Process - MDP) that assumes static environments and agent behavior, unsuitable for real-time applications.
• Existing methods for handling real-time delays, like interpolation or state augmentation, often require precise delay measurements and struggle with complex dynamics.

Purpose of the Study:

• To develop a novel real-time decision-making framework that accommodates concurrent changes in agent and environment dynamics.
• To introduce a reformulated MDP, the Minimal Information State Markov Decision Process (MISMDP), for real-time environments.
• To propose the Minimal Information Set for Real-time tasks using Actor-Critic (MRAC) algorithm to effectively manage delays.

Main Methods:

• Introduced a minimal information set to capture concurrent agent-environment interaction data.
• Reformulated the blocking-mode MDP into the MISMDP framework for real-time adaptability.
• Developed the MRAC algorithm within the MISMDP framework, including theoretical analysis of Q-function convergence.

Main Results:

• MRAC demonstrated superior performance and generalization capabilities compared to state-of-the-art algorithms in both discrete and continuous action space environments.
• The MISMDP framework effectively handles delays inherent in real-time tasks.
• Rigorous theoretical analysis supported the convergence of Q-functions within the MRAC approach.

Conclusions:

• The MISMDP framework and MRAC algorithm offer a robust solution for real-time reinforcement learning challenges, particularly those involving environmental delays.
• MRAC significantly advances the state-of-the-art in managing delays for improved agent performance in dynamic, real-time systems.