RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences

Core Concepts

RIME introduces a robust algorithm for PbRL, focusing on effective reward learning from noisy preferences. The approach incorporates a denoising discriminator and warm start method to enhance robustness and feedback efficiency.

Abstract

RIME presents a novel approach to improve the robustness of Preference-based Reinforcement Learning (PbRL) by addressing noisy preferences. The algorithm utilizes a denoising discriminator and warm start method to filter out corrupted samples and bridge the performance gap during training transitions. Experimental results demonstrate significant enhancements in robustness across various complex tasks, showcasing the effectiveness of RIME in noisy conditions. Key points: PbRL avoids reward engineering by using human preferences. Current PbRL algorithms lack robustness due to high-quality feedback reliance. RIME introduces a sample selection-based discriminator for robust training. Warm start is proposed to mitigate errors during transition phases. Experiments show RIME enhances robustness in robotic manipulation and locomotion tasks. Ablation studies confirm the importance of warm start for both robustness and feedback efficiency.

Stats

"Our experiments demonstrate that RIME exceeds existing baselines by a large margin in noisy conditions and considerably improves robustness." "The lack of robustness to noisy preference labels hinders the wide application of PbRL." "We propose to warm start the reward model, which additionally bridges the performance gap during transition from pre-training to online training in PbRL."

Quotes

"Our experiments demonstrate that RIME exceeds existing baselines by a large margin in noisy conditions and considerably improves robustness." "The lack of robustness to noisy preference labels hinders the wide application of PbRL." "We propose to warm start the reward model, which additionally bridges the performance gap during transition from pre-training to online training in PbRL."

Key Insights Distilled From

RIME

by Jie Cheng,Ga... at arxiv.org 03-13-2024

https://arxiv.org/pdf/2402.17257.pdf

Deeper Inquiries

How can RIME's approach be adapted for real-world applications with non-expert human teachers?

RIME's approach can be adapted for real-world applications with non-expert human teachers by leveraging its robustness to noisy preferences. In scenarios where non-experts provide feedback, the denoising discriminator in RIME can effectively filter out corrupted samples and identify trustworthy ones. This capability is crucial when dealing with inconsistent or erroneous labels from non-expert sources. Additionally, the warm-start method employed in RIME helps initialize the reward model, making it more adept at handling noisy data from non-experts. By combining these features, RIME ensures that the algorithm remains effective even when working with feedback from individuals who may not have expertise in the domain.

What are potential limitations or challenges when implementing RIME in more complex environments?

When implementing RIME in more complex environments, there are several potential limitations and challenges to consider: Increased Computational Complexity: As environments become more complex, the computational demands of training a denoising discriminator and incorporating a warm-start strategy may increase significantly. Scalability Issues: Scaling up RIME to handle larger datasets or higher-dimensional state spaces could pose challenges in terms of memory usage and processing power. Distribution Shifts: Complex environments often exhibit distribution shifts during training, which can impact the performance of sample selection methods like those used in RIME. Hyperparameter Tuning: Fine-tuning hyperparameters such as thresholds for filtering samples and decay rates for dynamic adjustments may become more challenging as complexity increases. Interpretability: In highly intricate environments, interpreting how decisions are made by the denoising discriminator could become less straightforward due to increased noise and variability. Addressing these limitations will be essential for successfully implementing RIME in diverse and complex real-world settings.

How does RIME's methodology compare with traditional reinforcement learning approaches outside of PbRL?

RIME's methodology differs from traditional reinforcement learning approaches outside of Preference-based RL (PbRL) primarily due to its focus on harnessing human preferences as rewards rather than explicitly defined reward functions engineered by experts or through trial-and-error exploration. Traditional RL algorithms rely on predefined reward signals that need careful design and tuning based on domain knowledge or task-specific objectives. In contrast, PbRL methods like Rime leverage human feedback to guide an agent’s learning process without requiring explicit reward engineering. By incorporating a denoising discriminator to filter noisy preferences and employing a warm-start strategy for seamless transition between pre-training and online training phases, Rime enhances robustness against errors introduced by imperfect feedback sources—a key challenge faced especially when dealing with non-expert human teachers. This adaptability makes it well-suited for scenarios where expert-designed rewards might be hard to define accurately upfront, and where relying solely on exploration-based techniques might not yield optimal results efficiently. Overall,Rime offers a unique perspective within PbRL that addresses issues related to noisy preference labels while maintaining efficiency and effectiveness across various tasks—an aspect not typically emphasized in traditional reinforcement learning paradigms beyond PbRL contexts.