Apr 6, 2022 · In this paper, we propose Policy Adaptation with Decoupled Representations (PAnDR) for fast policy adaptation.

In offline training phase, the environment representation and policy representation are learned through contrastive learning and policy recovery, respectively.

@inproceedings{ijcai2022p474, title = {PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations} ...

PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations · 1 code implementation • 6 Apr 2022 ...

In this paper, we propose Policy Adaptation with Decoupled Representations (PAnDR) for fast policy adaptation. In offline training phase, the environment ...

PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations · pdf icon · Sang Tong, Hongyao Tang ...

arXiv, 2022. PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations. Tong Sang, Hongyao ...

"PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations". breaking_news. Jan 28, 2022 ...

PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations. Deep Reinforcement Learning (DRL) ...

PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations · Published: 26 Apr 2022, Last Modified ...