Reinforcement Learning Utility
Tomer explains how agents receive rewards in reinforcement learning scenarios, optimizing for cumulative rewards. Kyle discusses the importance of providing agents with a coordinate system for spatial reasoning. Tomer introduces the concept of raycasts as a simpler observation space for reinforcement learning problems.In this clip
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