Safreni Candra Sari

About

Safreni Candra Sari is a researcher in artificial intelligence and multi-agent systems, with a focus on reinforcement learning and autonomous decision-making. Her work explores how agents can learn and act intelligently in dynamic environments, particularly in robotics and game-based simulations. In her 2012 paper on robosoccer agents, she applied the OODA Loop—a military decision-making framework—to enable agents to observe, orient, decide, and act based on real-time environmental changes, a foundational contribution to autonomous robotics. Her subsequent research advances reinforcement learning acceleration, notably through online state elimination and multi-agent coordination in gridworld soccer tasks. These contributions address critical challenges in scaling RL without relying on internal knowledge or human intervention. While her citation counts are modest, her work represents early, innovative steps in integrating cognitive models with machine learning for multi-agent systems. Sari’s research is particularly relevant for students and researchers interested in robotics, autonomous agents, and efficient reinforcement learning techniques.

Research Focus

Key Achievements

Top Papers

1. 1
   Decision system for robosoccer agent based on OODA Loop

   6 citations · 2012
2. 2
   Multi Agent Reinforcement Learning for Gridworld Soccer Leadingpass

   2 citations · 2013
3. 3
   Online State Elimination in Accelerated reinforcement Learning

   2 citations · 2014

Key Collaborators

Contact & Links