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Found 4 papers, 3 papers with code
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics
Finally, we investigate the application of multi-agent methods to high-dimensional robotic tasks and show that our approach can be used to learn decentralized policies in this domain.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Unsupervised Task Clustering for Multi-Task Reinforcement Learning
We show the generality of our approach by evaluating on simple discrete and continuous control tasks, as well as complex bipedal walker tasks and Atari games.
High-Resolution Image Editing via Multi-Stage Blended Diffusion
We first use Blended Diffusion to edit the image at a low resolution, and then upscale it in multiple stages, using a super-resolution model and Blended Diffusion.
Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains
In this paper, we investigate an offline reinforcement learning (RL) problem where datasets are collected from two domains.
