Yohei Hosoe
Papers
1
Total Citations
17
H-Index
1
About
Yohei Hosoe is a leading researcher in reinforcement learning (RL) and robotics control, with a particular focus on action-constrained decision-making. His most-cited work, "Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control With Action Constraints" (2023, 17 citations), establishes a rigorous evaluation framework for RL algorithms where each action must satisfy real-world safety and feasibility constraints. This benchmark is critical for deploying RL in physical systems, ensuring that learned policies respect hardware limits and operational boundaries. Hosoe’s contributions address a fundamental gap in RL research—moving from unconstrained simulations to practical, constraint-aware control. His work has direct implications for autonomous robotics, manufacturing, and any domain where unsafe actions could cause damage or harm. By providing standardized metrics and baselines, Hosoe enables reproducible comparisons and accelerates progress in safe RL. His research is essential reading for students and engineers seeking to bridge the gap between theoretical RL and real-world deployment, making him a key figure in the advancement of reliable, constraint-compliant autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1