Hybrid knowledge transfer for MARL based on action advising and experience sharing.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Multiagent Systems

Background:

• Multiagent Reinforcement Learning (MARL) effectively addresses complex decision-making problems.
• Existing knowledge transfer methods, like experience-sharing (KT-ES) and action-advising (KT-AA), have limitations in data efficiency, scalability, and error correction.

Purpose of the Study:

• To develop a hybrid knowledge transfer approach (KT-Hybrid) that overcomes the limitations of existing KT-ES and KT-AA methods.
• To enhance learning efficiency and performance in MARL by combining complementary knowledge transfer strategies.

Main Methods:

• Proposed KT-Hybrid approach utilizes KT-ES in the early learning phase for improved data efficiency.
• Employs KT-AA in the later learning phase to correct specific policy errors and refine performance.
• Simulations were conducted to evaluate the effectiveness of KT-Hybrid against established methods.

Main Results:

• KT-Hybrid demonstrated superior performance compared to standalone KT-AA and KT-ES methods.
• The hybrid approach achieved better data efficiency in early learning and more effective error correction in later stages.
• Simulations confirmed the advantages of the proposed KT-Hybrid strategy.

Conclusions:

• KT-Hybrid offers a significant advancement in knowledge transfer for MARL.
• The combined strategy effectively leverages the strengths of both experience-sharing and action-advising techniques.
• This approach provides a more robust and efficient solution for multiagent decision-making challenges.