Researchers from CMU and Peking Introduces ‘DiffTOP’ that Uses Differentiable Trajectory Optimization to Generate the Policy Actions for Deep Reinforcement Learning and Imitation Learning

According to recent studies, a policy’s depiction can significantly affect learning performance. Policy representations such as feed-forward neural networks, energy-based models, and diffusion have all been investigated in earlier research. A recent study by Carnegie Mellon University and Peking University researchers proposes producing actions for deep reinforcement and imitation learning using high-dimensional sensory data (images/point […]

The post Researchers from CMU and Peking Introduces ‘DiffTOP’ that Uses Differentiable Trajectory Optimization to Generate the Policy Actions for Deep Reinforcement Learning and …

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