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Adaptive Evolution Strategy with Ensemble of Mutations for Reinforcement Learning

Knowledge-Based Systems(2022)CCF CSCI 1区

Kyungpook Natl Univ

Cited 17|Views30
Abstract
Evolving the weights of learning networks through evolutionary computation (neuroevolution) has proven scalable over a range of challenging Reinforcement Learning (RL) control tasks. However, similar to most black-box optimization problems, existing neuroevolution approaches require an additional adaptation process to effectively balance exploration and exploitation through the selection of sensitive hyper-parameters throughout the evolution process. Therefore, these methods are often plagued by the computation complexities of such adaptation processes which often rely on a number of sophisticatedly formulated strategy parameters. In this paper, Evolution Strategy (ES) with a simple yet efficient ensemble of mutation strategies is proposed. Specifically, two distinct mutation strategies coexist throughout the evolution process where each strategy is associated with its own population subset. Consequently, elites for generating a population of offspring are realized by co-evaluation of the combined population. Experiments on testbed of six (6) black-box optimization problems which are generated using a classical control problem and six (6) proven continuous RL agents demonstrate the efficiency of the proposed method in terms of faster convergence and scalability than the canonical ES. Furthermore, the proposed Adaptive Ensemble ES (AEES) shows an average of 5 - 10000x and 10 - 100x better sample complexity in low and high dimension problems, respectively than their associated base DRL agents.
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Evolution strategy,Reinforcement Learning,Ensemble,Mutation strategy,Black-box optimization
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Chat Paper

要点】:本文提出了一种适应性进化策略的增强学习方法,通过集成多种变异策略来平衡探索与利用,提高了收敛速度和样本效率,对比经典进化策略有显著优势。

方法】:该方法采用了两种不同的变异策略,在整个进化过程中共存,每个策略都与自己的种群子集相关联,通过共同评估组合种群来实现精英生成。

实验】:在六个基于经典控制问题生成的黑盒优化问题测试床以及六个经过验证的连续强化学习代理上进行的实验表明,所提出的方法在收敛速度和可扩展性方面优于标准进化策略。此外,与相应的基于深度强化学习的基准代理相比,所提出的自适应集成进化策略(AEES)在低维和高维问题上分别平均具有5-10000倍和10-100倍的更好样本复杂性。