Authors: Zelin Wan, Jin-Hee Cho, Mu Zhu, Ahmed H. Anwar, Charles Kamhoua, Munindar P. Singh

Published on: February 08, 2024

Impact Score: 8.15

Arxiv code: Arxiv:2402.06023

Summary

• What is new: Introduction of Decision Theory-guided Deep Reinforcement Learning (DT-guided DRL) to enhance initial performance and robustness of agents in complex environments.
• Why this is important: The inherent cold start problem in Deep Reinforcement Learning where agents struggle with initial performance in complex environments.
• What the research proposes: Integrating decision theory with DRL to provide initial guidance and structure for agents, leading to better performance and efficiency.
• Results: DT-guided DRL showed up to an 184% increase in accumulated reward during training’s initial phase and maintained up to 53% more reward than standard DRL after convergence in complex environments.

Technical Details

Technological frameworks used: Decision Theory-guided Deep Reinforcement Learning

Models used: Applied to cart pole and maze navigation challenges

Data used: Complex environments characterized by large and intricate state spaces

Potential Impact

Gaming industry, autonomous vehicle developers, robotics companies, and AI-driven system developers could benefit or face disruption due to these insights

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