Haotian Miao
Papers
1
Total Citations
35
H-Index
1
About
Haotian Miao is a rising researcher in artificial intelligence, with a primary focus on multi-agent reinforcement learning and its applications in collaborative robotics and game AI. His most-cited work, “A Review of Multi-Agent Reinforcement Learning Algorithms” (2025, 35 citations), offers a comprehensive synthesis of single-agent and multi-agent system modeling, grounded in the foundational principles of Markov Decision Processes. This review has become a key reference for researchers seeking to understand the evolution from individual to collective decision-making in complex environments. Miao’s contributions lie in clarifying the theoretical frameworks that enable agents to learn coordinated strategies, addressing challenges such as non-stationarity and scalability. Beyond this review, his work is shaping how autonomous systems can achieve robust cooperation, with implications for real-world deployments in robotics and interactive AI. With a growing citation footprint and a clear trajectory toward impactful, application-driven research, Miao is establishing himself as a thoughtful voice in the next wave of intelligent multi-agent systems.
Research Focus
Key Achievements
Top Papers
- 1A Review of Multi-Agent Reinforcement Learning Algorithms35 citations · 2025