1 citations · 2 across the 10 of their papers we have counts for
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Generative Actor Critic
Aoyang Qin, Deqian Kong, Wei Wang +3
Conventional Reinforcement Learning (RL) algorithms, typically focused on estimating or maximizing expected returns, face challenges when refining offline pretrained models with on…
DODT: Enhanced Online Decision Transformer Learning through Dreamer's Actor-Critic Trajectory Forecasting
Eric Hanchen Jiang, Zhi Zhang, Dinghuai Zhang +9
Advancements in reinforcement learning have led to the development of sophisticated models capable of learning complex decision-making tasks. However, efficiently integrating world…
Latent Space Energy-based Neural ODEs
Sheng Cheng, Deqian Kong, Jianwen Xie +3
This paper introduces novel deep dynamical models designed to represent continuous-time sequences. Our approach employs a neural emission model to generate each data point in the t…
Latent Plan Transformer for Trajectory Abstraction: Planning as Latent Space Inference
Deqian Kong, Dehong Xu, Minglu Zhao +6
In tasks aiming for long-term returns, planning becomes essential. We study generative modeling for planning with datasets repurposed from offline reinforcement learning. Specifica…