Key Responsibilities
Applied multi-agent reinforcement learning to develop an improved path-finding system for simulations
Designed and tested Unity simulations in C# to enhance AI decision-making and coordination
Applied machine learning to train agents for complex tasks and optimize their behavior
Collaborated on reports and contributed to academic discussions, enhancing the quality of research outputs
Notable Achievements
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Improved multi-agent reinforcement learning performance through reward shaping techniques inspired by video game design principles
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Demonstrated coordinated behavior among multiple agents in Unity simulations, validating reinforcement learning strategies for complex tasks