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Found 4 papers, 1 papers with code
A Unified Linear Programming Framework for Offline Reward Learning from Human Demonstrations and Feedback
Inverse Reinforcement Learning (IRL) and Reinforcement Learning from Human Feedback (RLHF) are pivotal methodologies in reward learning, which involve inferring and shaping the underlying reward function of sequential decision-making problems based on observed human demonstrations and feedback.
Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural Network
We propose a novel approach to optimize fleet management by combining multi-agent reinforcement learning with graph neural network.
Generative Autoregressive Networks for 3D Dancing Move Synthesis from Music
The trained dance pose generator, which is a generative autoregressive model, is able to synthesize a dance sequence longer than 5, 000 pose frames.
Differential Generative Adversarial Networks: Synthesizing Non-linear Facial Variations with Limited Number of Training Data
through a generative model is helpful in addressing the lack of training data.
