Papers
39
Total Citations
510
H-Index
12
About
Reinaldo A. C. Bianchi is a Brazilian researcher whose work sits at a compelling intersection of reinforcement learning, case-based reasoning, and autonomous robotics. His most influential contribution — the framework of Heuristically Accelerated Reinforcement Learning (HARL) — addresses one of the central challenges in machine learning: how to make autonomous agents learn faster and more efficiently. By leveraging heuristics derived from case-based reasoning and transfer learning, Bianchi's methods dramatically reduce the time agents need to acquire competent behavior, as demonstrated across multiple papers accumulating over 250 citations combined. His 2007 foundational paper on accelerating autonomous learning remains his most cited work, establishing the theoretical and practical groundwork that subsequent studies refined and extended into multiagent and transfer learning settings. Beyond algorithmic contributions, Bianchi has made significant applied contributions to humanoid robotics, developing vision systems, terrain classification using convolutional neural networks, and walking strategies — much of this work validated through participation in the prestigious RoboCup competition. His career reflects a rare blend of theoretical rigor and real-world engineering, making him a valuable reference for researchers working in intelligent agents, robot learning, and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Accelerating autonomous learning by using heuristic selection of actions79 citations · 2007
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- 3Qualitative case-based reasoning and learning42 citations · 2020
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- 5Heuristic Reinforcement Learning Applied to RoboCup Simulation Agents25 citations · 2008
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- 7RoboCup 2014: Robot World Cup XVIII18 citations · 2015
- 8A Single Camera Vision System for a Humanoid Robot17 citations · 2014
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