BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel Optimization

Autor:	Wang, Junyi, Zhu, Yuanyang, Wang, Zhi, Zheng, Yan, Hao, Jianye, Chen, Chunlin
Rok vydání:	2023
Předmět:	Computer Science - Neural and Evolutionary Computing Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Evolutionary reinforcement learning (ERL) algorithms recently raise attention in tackling complex reinforcement learning (RL) problems due to high parallelism, while they are prone to insufficient exploration or model collapse without carefully tuning hyperparameters (aka meta-parameters). In the paper, we propose a general meta ERL framework via bilevel optimization (BiERL) to jointly update hyperparameters in parallel to training the ERL model within a single agent, which relieves the need for prior domain knowledge or costly optimization procedure before model deployment. We design an elegant meta-level architecture that embeds the inner-level's evolving experience into an informative population representation and introduce a simple and feasible evaluation of the meta-level fitness function to facilitate learning efficiency. We perform extensive experiments in MuJoCo and Box2D tasks to verify that as a general framework, BiERL outperforms various baselines and consistently improves the learning performance for a diversity of ERL algorithms. Comment: Published as a conference paper at European Conference on Artificial Intelligence (ECAI) 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.01207 Zobrazit plný text záznamu View this record from Arxiv