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Feature-Based vs. GAN-Based Imitation: When and Why The main difference lies in the signal strength Feature-based methods align reference and policy trajectories frame-by-frame, providing a dense and informative signal GAN-based rewards are coarser, leading to better adaptability in some tasks, but typically lower fidelity to the reference motion
Robust Learning from Demonstration Based on GANs and Affine . . . - MDPI Our results demonstrate that our proposed method significantly accelerates generation speed, achieving a remarkable processing time of 23 ms, which is five times faster than movement primitives (MPs), while preserving key features from demonstrations
GAN-Based Interactive Reinforcement Learning from Demonstration and . . . aluative feedback by combining the advantages of GAIL and interactive reinforcement learning We tested our proposed method in six physics-based control tasks, ranging from simple low-dimensional control tasks — Cart Pole and Mountain Car, to diffic
Human-Guided Robot Behavior Learning: A GAN-Assisted Preference-Based . . . To reduce and minimize the need for human queries, we propose a new GAN-assisted human preference-based reinforcement learning approach that uses a generative adversarial network (GAN) to learn human preferences and then replace the role of human in assigning preferences