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Found 3 papers, 3 papers with code
Decoupling Meta-Reinforcement Learning with Gaussian Task Contexts and Skills
We propose a framework called decoupled meta-reinforcement learning (DCMRL), which (1) contrastively restricts the learning of task contexts through pulling in similar task contexts within the same task and pushing away different task contexts of different tasks, and (2) utilizes a Gaussian quantization variational autoencoder (GQ-VAE) for clustering the Gaussian distributions of the task contexts and skills respectively, and decoupling the exploration and learning processes of their spaces.
Step by step: a hierarchical framework for multi-hop knowledge graph reasoning with reinforcement learning
Due to this one-to-many dilemma, enlarged action space and ignoring logical relationship between entity and relation increase the difficulty of learning.
SMDT: Cross-View Geo-Localization with Image Alignment and Transformer
Next, we simultaneously train dual conditional generative adversarial nets by taking the semantic segmentation images and converted images as input to synthesize the aerial image with ground view style.
