BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel Optimization

• URL: http://arxiv.org/abs/2308.01207v1
• Date: Tue, 1 Aug 2023 09:31:51 GMT
• Title: BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel Optimization
• Authors: Junyi Wang, Yuanyang Zhu, Zhi Wang, Yan Zheng, Jianye Hao, Chunlin Chen
• Abstract summary: We propose a general meta ERL framework via bilevel optimization (BiERL) We design an elegant meta-level architecture that embeds the inner-level's evolving experience into an informative population representation. 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.
• Score: 34.24884427152513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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