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

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 >> 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 >> 1.

• The paper demonstrates REDQ's primary contribution in reducing sample complexity by leveraging high update-to-data ratios.
• It employs an ensemble of Q functions with in-target minimization to mitigate overestimation bias and variance.
• Experimental results on MuJoCo benchmarks reveal that REDQ outperforms standard model-free methods while rivaling model-based approaches.

Insights into Randomized Ensembled Double Q-Learning

The paper presents a novel model-free reinforcement learning algorithm called Randomized Ensembled Double Q-Learning (REDQ), focusing on enhancing sample efficiency in continuous-action domains. REDQ is a noteworthy contribution to the landscape of Deep Reinforcement Learning (DRL) methods, traditionally dominated by model-based approaches to achieve high sample efficiency.

Key Contributions and Methodology

REDQ is distinguished by its utilization of a high Update-To-Data (UTD) ratio, a strategy commonly associated with model-based methods. The algorithm incorporates three core elements that synergistically contribute to its performance:

1. High UTD Ratios: Unlike traditional model-free algorithms that employ a UTD ratio of one, REDQ opts for a significantly larger ratio, aligning its update frequency closer to that of model-based methods, thereby improving sample efficiency.
2. Ensemble of Q Functions: REDQ leverages an ensemble of Q functions. This ensemble setup helps in imparting stability to the learning process by averaging out the variances that individual networks might encounter during updates.
3. In-Target Minimization: The approach selects a random subset of Q functions from the ensemble to perform minimization, reducing overestimation bias. This method marks a departure from traditional Double Q-learning in its randomization process, providing a robust mechanism to control bias.

The ensemble approach also ensures a reduction in variance, which is pivotal in maintaining Q-function robustness during updates involving high UTD ratios. REDQ's structure allows it to harness the strengths of ensemble learning and bias minimization, both crucial for optimal estimation in continuous action spaces.

Experimental Findings

The research showcases REDQ's competitive performance on the MuJoCo benchmark. REDQ not only surpasses existing model-free algorithms in sample efficiency but also equates or eclipses the state-of-the-art model-based algorithms such as Model-Based Policy Optimization (MBPO) while utilizing fewer parameters and requiring less computational time.

Empirical results delineate REDQ's efficacy in environments like Hopper, Walker2d, Ant, and Humanoid. The algorithm's sample efficiency is validated by achieving benchmark performances with notably fewer interactions than standard methods. Additionally, the runtime analysis indicates a reduction in computational complexity, broadening REDQ's applicability to scenarios where resource constraints are pertinent.

Theoretical Implications and Analysis

A significant contribution is the theoretical framework analyzing REDQ's bias management. The paper explores the variance and bias implications stemming from its ensemble strategy and in-target minimization. It demonstrates how REDQ effectively maintains a low and consistent bias across training episodes, which is crucial for reliable and stable learning processes.

REDQ’s theoretical backing is further solidified by proving its convergence to the optimal Q function under its tabular version. This assertion is pivotal as it offers a theoretical guarantee of performance, underpinning the empirical observations with a formal proof of concept.

Future Directions and Impact

The introduction of REDQ suggests several avenues for future investigation. The interplay between ensemble size, UTD ratios, and bias control could unlock more refined algorithms capable of even higher efficiencies. Furthermore, augmenting REDQ with auxiliary features, as explored with OFENet in this research, highlights the potential for extensions and hybrid methods that enhance representation learning.

Moreover, the study prompts a reevaluation of the necessity and role of model-based approaches in certain benchmark domains. REDQ’s framework could potentially encourage a paradigm shift, where model-free algorithms are further explored and optimized, narrowing the performance gap.

Conclusion

The development of REDQ marks an advancement in the landscape of model-free reinforcement learning algorithms by successfully bridging the gap in sample efficiency typically dominated by model-based approaches. The convergence of innovative ensemble strategies and bias management provides a robust model-free alternative that is both computationally efficient and theoretically sound.