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A team of researchers at Northwestern University has developed a new algorithm, called Maximum Diffusion Reinforcement Learning (MaxDiff RL), that is specifically tailored for robots. This algorithm focuses on training end states rather than the process, making it more efficient for real-world applications. It also introduces chaos to the learning process, allowing for more diverse and realistic data. This could be a transformative development for embodied AI in the real world.
