Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics

Abstract: The overestimation bias is one of the major impediments to accurate off-policy learning. This paper investigates a novel way to alleviate the overestimation bias in a continuous control setting. Our method---Truncated Quantile Critics, TQC,---blends three ideas: distributional representation of a critic, truncation of critics prediction, and ensembling of multiple critics. Distributional representation and truncation allow for arbitrary granular overestimation control, while ensembling provides additional score improvements. TQC outperforms the current state of the art on all environments from the continuous control benchmark suite, demonstrating 25% improvement on the most challenging Humanoid environment.

• The paper introduces Truncated Quantile Critics (TQC), a novel method combining distributional representation, truncation, and ensembling to control overestimation bias in continuous control reinforcement learning.
• TQC demonstrates superior performance, achieving significant improvements (up to 30% in some cases, 25% in Humanoid) over baseline methods in various continuous control tasks.
• This research provides a refined tool for practical real-world continuous control applications like robotics and automated systems, while also opening theoretical avenues for leveraging distributional perspectives and uncertainty modeling.

Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics

The paper "Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics" addresses the complex issue of overestimation bias in off-policy reinforcement learning, particularly within continuous control settings. The primary objective is to enhance sample efficiency by optimizing the approximation of the Q-function, an essential component for stability and performance in reinforcement learning models.

Methodology

The authors introduce a novel technique named Truncated Quantile Critics (TQC), which integrates three elements:

1. Distributional Representation: This approach focuses on approximating the distribution of possible returns rather than merely the expected return, thus capturing the inherent uncertainty within a reinforcement learning environment.
2. Truncation: By strategically truncating the right tail of the predicted return distribution, the method finely controls overestimation, dropping just a subset of atoms—approximately 8%—to balance between underestimation and overestimation.
3. Ensembling: Multiple critic networks are employed, their outputs pooled to form a comprehensive distribution that is then truncated, allowing for enhanced performance through aggregation of predictions.

The paper substantiates the claim that the distributional representation aids in understanding the aleatoric uncertainty, and truncation effectively mitigates inflated overestimation due to high variance in returns.

Key Findings

TQC demonstrates superior performance across various challenging environments, providing substantial improvement—in some cases up to 30% over baseline methods—in continuous control tasks. Notably, in the Humanoid environment, the method achieves a 25% improvement over existing approaches. This reflects the method's effectiveness in applications requiring high precision and control in reinforcement learning tasks.

Implications

The implications of this research are multifaceted. Practically, TQC offers a refined toolset for addressing overestimation bias, which can be pivotal in training reinforcement learning models in real-world applications where continuous control is required—examples being robotics and automated systems. Theoretically, this work opens avenues for further research into the understanding and exploitation of distributional perspectives in reinforcement learning, particularly focusing on how aleatoric uncertainty can be effectively utilized for bias control.

Future Directions

Further exploration is suggested in matters concerning the relationship between uncertainty modeling and bias mitigation. Investigation into alternative methodologies that leverage distributional approximations to improve policy stability and efficiency in various control environments may yield additional advancements. Moreover, extending these approaches to distributed and concurrent reinforcement learning settings could significantly enhance scalability and robustness.

In conclusion, the paper offers a compelling contribution to the ongoing discourse on reinforcement learning optimization, presenting a robust solution to a well-recognized challenge in the domain. The Truncated Quantile Critics method stands as a promising strategy for practitioners and researchers aiming to refine continuous control policies within the diverse spectrum of artificial intelligence applications.