Model Learning and Knowledge Sharing for Cooperative Multiagent Systems in Stochastic Environment.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning

Background:

• Reinforcement learning agents face challenges in learning optimal policies quickly in uncertain environments.
• Efficiently modeling stochastic environments is crucial for effective agent learning and decision-making.

Purpose of the Study:

• To develop an incremental model learning scheme for reconstructing stochastic environment models.
• To accelerate reinforcement learning through enhanced exploration-exploitation strategies and planning.
• To extend the learning scheme for multiagent systems to improve exploration efficiency and reduce learning time.

Main Methods:

• Introduced a clustering algorithm to assimilate model information and estimate state transition probabilities.
• Implemented an experience replay strategy using the reconstructed model to create virtual experiences, balancing exploration and exploitation.
• Developed a multiagent framework with knowledge-sharing and efficient knowledge-fusing mechanisms for collaborative learning.

Main Results:

• The proposed incremental model learning scheme effectively reconstructs stochastic environment models.
• The experience replay strategy significantly accelerates learning and enables planning capabilities.
• The multiagent extension demonstrated reduced exploration effort and faster learning in complex tasks.

Conclusions:

• The presented methods provide an effective approach for reinforcement learning in uncertain and complex environments.
• The incremental model learning and multiagent extensions offer significant improvements in learning efficiency and scalability.