  Multi-task reinforcement learning and meta-reinforcement learning have been
developed to quickly adapt to new tasks, but they tend to focus on tasks with
higher rewards and more frequent occurrences, leading to poor performance on
tasks with sparse rewards. To address this issue, GFlowNets can be integrated
into meta-learning algorithms (GFlowMeta) by leveraging the advantages of
GFlowNets on tasks with sparse rewards. However, GFlowMeta suffers from
performance degradation when encountering heterogeneous transitions from
distinct tasks. To overcome this challenge, this paper proposes a personalized
approach named pGFlowMeta, which combines task-specific personalized policies
with a meta policy. Each personalized policy balances the loss on its
personalized task and the difference from the meta policy, while the meta
policy aims to minimize the average loss of all tasks. The theoretical analysis
shows that the algorithm converges at a sublinear rate. Extensive experiments
demonstrate that the proposed algorithm outperforms state-of-the-art
reinforcement learning algorithms in discrete environments.
