Decoupling Meta-Reinforcement Learning with Gaussian Task Contexts and Skills

1 code implementation • 11 Dec 2023 • Hongcai He, Anjie Zhu, Shuang Liang, Feiyu Chen, Jie Shao

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.

Continuous Control Meta Reinforcement Learning +3