Ivan Letteri
Papers
1
Total Citations
4
H-Index
1
About
Ivan Letteri is a researcher at the intersection of artificial intelligence, procedural content generation, and deep reinforcement learning. His work focuses on developing novel methodologies for training and benchmarking autonomous agents, particularly through the creation of complex, procedurally generated environments. In his most-cited paper, "Extension of constraint-procedural logic-generated environments for deep Q-learning agent training and benchmarking" (2023, 4 citations), Letteri advances the state of the art by extending constraint-procedural logic to generate diverse and challenging virtual spaces. This approach enables more robust training of deep Q-learning agents, allowing them to generalize better across unseen scenarios—a critical step toward deploying autonomous robots in real-world exploration and object detection tasks. By bridging procedural generation with reinforcement learning benchmarks, Letteri provides researchers with powerful tools to systematically evaluate agent performance. His work contributes to the broader goal of creating more adaptable and intelligent autonomous systems, with implications for robotics, simulation, and AI safety.
Research Focus
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Top Papers
- 1