GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models

no code implementations • 30 Oct 2023 • Mianchu Wang, Rui Yang, Xi Chen, Hao Sun, Giovanni Montana, Meng Fang

In this work, we propose Goal-conditioned Offline Planning (GOPlan), a novel model-based framework that contains two key phases: (1) pretraining a prior policy capable of capturing multi-modal action distribution within the multi-goal dataset; (2) employing the reanalysis method with planning to generate imagined trajectories for funetuning policies.

Generative Adversarial Network reinforcement-learning

Goal-conditioned Offline Reinforcement Learning through State Space Partitioning

no code implementations • 16 Mar 2023 • Mianchu Wang, Yue Jin, Giovanni Montana

Offline reinforcement learning (RL) aims to infer sequential decision policies using only offline datasets.

Inductive Bias reinforcement-learning +1