変分因果推論による目標条件付き強化学習の汎化
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning
因果グラフを潜在変数として目標条件付き強化学習を変分最適化し、因果発見と方策学習を交互に行うことで未知の目標への汎化性能を高める手法を提案。
著者: Wenhao Ding, Haohong Lin, Bo Li, Ding Zhao
分類: cs.LG, cs.AI, cs.RO, stat.ME
原文アブストラクト
As a pivotal component to attaining generalizable solutions in human intelligence, reasoning provides great potential for reinforcement learning (RL) agents' generalization towards varied goals by summarizing part-to-whole arguments and discovering cause-and-effect relations. However, how to discover and represent causalities remains a huge gap that hinders the development of causal RL. In this paper, we augment Goal-Conditioned RL (GCRL) with Causal Graph (CG), a structure built upon the relation between objects and events. We novelly formulate the GCRL problem into variational likelihood maximization with CG as latent variables. To optimize the derived objective, we propose a framework with theoretical performance guarantees that alternates between two steps: using interventional data to estimate the posterior of CG; using CG to learn generalizable models and interpretable policies. Due to the lack of public benchmarks that verify generalization capability under reasoning, we design nine tasks and then empirically show the effectiveness of the proposed method against five baselines on these tasks. Further theoretical analysis shows that our performance improvement is attributed to the virtuous cycle of causal discovery, transition modeling, and policy training, which aligns with the experimental evidence in extensive ablation studies.
