Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

Xinyue Chen, Che Wang, Zijian Zhou, Keith W. Ross

Keywords: Artificial Integlligence, Machine Learning, Deep Reinforcement Learning

Abstract: Using a high Update-To-Data (UTD) ratio, model-based methods have recently achieved much higher sample efficiency than previous model-free methods for continuous-action DRL benchmarks. In this paper, we introduce a simple model-free algorithm, Randomized Ensembled Double Q-Learning (REDQ), and show that its performance is just as good as, if not better than, a state-of-the-art model-based algorithm for the MuJoCo benchmark. Moreover, REDQ can achieve this performance using fewer parameters than the model-based method, and with less wall-clock run time. REDQ has three carefully integrated ingredients which allow it to achieve its high performance: (i) a UTD ratio $\gg 1$; (ii) an ensemble of Q functions; (iii) in-target minimization across a random subset of Q functions from the ensemble. Through carefully designed experiments, we provide a detailed analysis of REDQ and related model-free algorithms. To our knowledge, REDQ is the first successful model-free DRL algorithm for continuous-action spaces using a UTD ratio $\gg 1$.

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One-sentence Summary: We propose and analyze a novel model-free algorithm that achieves strong performance with a high update-to-data ratio.

Code: [ watchernyu/REDQ](https://github.com/watchernyu/REDQ) + [ 5 community implementations](https://paperswithcode.com/paper/?openreview=AY8zfZm0tDd)

Data: [MuJoCo](https://paperswithcode.com/dataset/mujoco)

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2101.05982/code)