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The heterophilic graph learning handbook: Benchmarks, models, theoretical analysis, applications and challenges
Provably efficient offline reinforcement learning with trajectory-wise reward
The remarkable success of reinforcement learning (RL) heavily relies on observing the
reward of every visited state-action pair. In many real world applications, however, an agent …
reward of every visited state-action pair. In many real world applications, however, an agent …
Neural algorithmic reasoners are implicit planners
Implicit planning has emerged as an elegant technique for combining learned models of the
world with end-to-end model-free reinforcement learning. We study the class of implicit …
world with end-to-end model-free reinforcement learning. We study the class of implicit …
Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing
Recent research has shown that graph neural networks (GNNs) can learn policies for
locomotion control that are as effective as a typical multi-layer perceptron (MLP), with …
locomotion control that are as effective as a typical multi-layer perceptron (MLP), with …
On addressing the limitations of graph neural networks
S Luan - arxiv preprint arxiv:2306.12640, 2023 - arxiv.org
Sitao proposal Page 1 On Addressing the Limitations of Graph Neural Networks Sitao Luan1,2
1sitao.luan@maill.mcgill.ca 1McGill University; 2Mila July 4, 2023 Abstract This report gives a …
1sitao.luan@maill.mcgill.ca 1McGill University; 2Mila July 4, 2023 Abstract This report gives a …
Reward sha** with hierarchical graph topology
J Sang, Y Wang, W Ding, Z Ahmadkhan, L Xu - Pattern Recognition, 2023 - Elsevier
Reward sha** using GCNs is a popular research area in reinforcement learning.
However, it is difficult to shape potential functions for complicated tasks. In this paper, we …
However, it is difficult to shape potential functions for complicated tasks. In this paper, we …