ロバスト交叉エントロピー法による制約付きモデルベース強化学習
Constrained Model-based Reinforcement Learning with Robust Cross-Entropy Method
モデル予測制御とニューラルネットワークアンサンブルで不確実性を見積もり、制約違反を抑えながら効率的に学習する安全強化学習手法を提案した。
著者: Zuxin Liu, Hongyi Zhou, Baiming Chen, Sicheng Zhong, Martial Hebert, Ding Zhao
分類: cs.AI, cs.LG, cs.RO
原文アブストラクト
This paper studies the constrained/safe reinforcement learning (RL) problem with sparse indicator signals for constraint violations. We propose a model-based approach to enable RL agents to effectively explore the environment with unknown system dynamics and environment constraints given a significantly small number of violation budgets. We employ the neural network ensemble model to estimate the prediction uncertainty and use model predictive control as the basic control framework. We propose the robust cross-entropy method to optimize the control sequence considering the model uncertainty and constraints. We evaluate our methods in the Safety Gym environment. The results show that our approach learns to complete the tasks with a much smaller number of constraint violations than state-of-the-art baselines. Additionally, we are able to achieve several orders of magnitude better sample efficiency when compared with constrained model-free RL approaches. The code is available at \url{https://github.com/liuzuxin/safe-mbrl}.
