Sample-Efficient Preference-based Reinforcement Learning with Dynamics-Aware Rewards

核心概念

The author argues that dynamics-aware reward functions significantly improve the sample efficiency of preference-based reinforcement learning, leading to faster policy learning and better final policy performance.

摘要

The content discusses the benefits of dynamics-awareness in preference-based reinforcement learning. It introduces the concept of Preference-based RL (PbRL), explores dynamics-aware reward functions, and presents experimental results demonstrating the effectiveness of these methods across various tasks and feedback amounts.

The authors highlight the challenges of specifying reliable numerical reward functions in traditional reinforcement learning and introduce PbRL as a solution that infers reward values from preference feedback. They propose using dynamics-aware reward functions to improve sample efficiency in PbRL by incorporating environment dynamics into the learning process.

Through experiments on locomotion tasks and object manipulation tasks, they show that REED (Rewards Encoding Environment Dynamics) outperforms existing methods like SURF, RUNE, and MRN in terms of policy performance. The results indicate that REED methods retain policy performance with significantly fewer pieces of feedback compared to baseline approaches.

The study also compares different labelling strategies for preference feedback and analyzes the impact of image-space observations on policy performance. The authors conclude that dynamics awareness is crucial for improving sample efficiency in preference-based reinforcement learning.

統計資料

For example, on quadruped-walk, walker-walk, and cheetah-run, with 50 preference labels we achieve the same performance as existing approaches with 500 preference labels.
We recover 83% and 66% of ground truth reward policy performance versus only 38% and 21%.

引述

"We show that dynamics-aware reward functions improve the sample efficiency of PbRL by an order of magnitude."
"REED methods consistently outperform SURF, RUNE, and MRN on DMC tasks demonstrating the importance of dynamics awareness for locomotion tasks."

從以下內容提煉的關鍵洞見

Sample-Efficient Preference-based Reinforcement Learning with Dynamics Aware Rewards

by Katherine Me... 於 arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.17975.pdf

深入探究

How can dynamics-awareness be further integrated into other areas of robotics beyond reinforcement learning?

Dynamics-awareness can be applied in various ways across different areas of robotics to enhance performance and efficiency. In robot control, understanding the dynamics of the environment can lead to more robust and adaptive controllers that can adjust to changing conditions. For motion planning, incorporating dynamics-aware models can help predict how a robot's actions will affect its surroundings, leading to safer and more efficient paths. In robotic manipulation tasks, considering environment dynamics can improve grasp stability and object manipulation accuracy. Additionally, in autonomous navigation systems, accounting for dynamic obstacles and environmental changes can result in smoother trajectories and better collision avoidance strategies.

What potential drawbacks or limitations might arise from relying heavily on human preferences for training policies?

While leveraging human preferences for training policies has several advantages such as interpretability and adaptability to user needs, there are also potential drawbacks to consider:

Subjectivity: Human preferences may vary widely among individuals, leading to inconsistencies in feedback.
Bias: Human feedback may introduce bias based on personal experiences or expectations.
Limited Feedback: Collecting sufficient preference data from humans may be time-consuming and costly.
Noise: Human feedback is prone to errors or misinterpretations which could impact the quality of learned policies.
Generalization: Policies trained solely on human preferences may struggle with generalizing well beyond the provided feedback data.

How might advancements in self-supervised learning techniques impact the future development of PbRL systems?

Advancements in self-supervised learning techniques have the potential to significantly impact PbRL systems by addressing key challenges such as sample efficiency and generalization:

Improved Data Efficiency: Self-supervised learning methods enable PbRL systems to learn representations from unlabeled data efficiently, reducing reliance on labeled samples.
Better Generalization: By pre-training models using self-supervision tasks like temporal consistency prediction or contrastive learning, PbRL systems can capture underlying structure in data for improved generalization.
Robust Feature Learning: Self-supervised techniques help extract meaningful features from raw sensor inputs without requiring explicit supervision signals.
Reduced Annotation Costs: With self-supervision methods eliminating the need for extensive manual labeling efforts, PbRL systems become more cost-effective and scalable.

These advancements pave the way for more effective policy learning with reduced dependency on external supervision sources while enhancing adaptability across diverse robotic applications within Preference-based Reinforcement Learning frameworks like those discussed above (PbRL).