Leveraging Abundant Sub-Optimal Data to Improve Feedback Efficiency in Human-in-the-Loop Reinforcement Learning

핵심 개념

Leveraging abundant sub-optimal, reward-free data can significantly improve the feedback efficiency of both scalar- and preference-based human-in-the-loop reinforcement learning algorithms.

초록

The paper presents Sub-optimal Data Pre-training (SDP), a method to leverage abundant sub-optimal, reward-free data to improve the feedback efficiency of human-in-the-loop (HitL) reinforcement learning (RL) algorithms.
The key insights are:

SDP pseudo-labels all sub-optimal transitions with a reward of zero, which provides a "free" head start for the reward model to learn that low-quality transitions should have a low reward.

SDP initializes the RL agent's replay buffer with the pseudo-labeled sub-optimal data, which changes the agent's policy and generates new behaviors for the human teacher to provide feedback on.

Extensive experiments on both scalar- and preference-based HitL RL algorithms across robotic manipulation and locomotion tasks show that SDP can significantly improve feedback efficiency compared to state-of-the-art baselines.

SDP is flexible and can leverage sub-optimal data from tasks other than the target task, further highlighting its generality.

Ablation studies demonstrate the importance of both phases of SDP (reward model pre-training and agent update) as well as the benefits of using true sub-optimal transitions versus "fake" data.

Overall, this work takes an important step towards leveraging readily-available sub-optimal data to improve the sample efficiency of HitL RL approaches.

통계

"To create useful reinforcement learning (RL) agents, step zero is to design a suitable reward function that captures the nuances of the task."
"As the complexity of tasks increases, so does the time and effort required to design a suitable reward function."
"Existing preference- and scalar-based HitL RL methods still suffer from high human labeling costs that can require thousands of human queries to learn an adequate reward function."

인용구

"Can we leverage abundant sub-optimal, unlabeled data to improve learning in HitL RL methods?"
"The core contribution is showing that we can harness the availability of low-quality, reward-free data for HitL RL approaches by pseudo-labeling it with zero rewards and treating it as a free bias for learning reward models."

핵심 통찰 요약

Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning

by Calarina Mus... 게시일 arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.00746.pdf

더 깊은 질문

How can we extend SDP to leverage sub-optimal data from multiple tasks simultaneously to further improve feedback efficiency

To extend SDP to leverage sub-optimal data from multiple tasks simultaneously, we can modify the pre-training phase to incorporate data from various tasks. Instead of pseudo-labeling transitions from a single task with zero rewards, we can aggregate sub-optimal data from different tasks and assign them with task-specific minimum rewards. This approach would involve creating a diverse dataset with transitions from multiple tasks, each labeled with the minimum reward for that specific task. By training the reward model on this combined dataset, the model can learn to differentiate between sub-optimal transitions from different tasks and assign appropriate reward values accordingly. This method would allow SDP to leverage a broader range of sub-optimal data, potentially improving the feedback efficiency across multiple tasks simultaneously.

What are the potential drawbacks or limitations of relying on sub-optimal data, and how can we mitigate them

One potential drawback of relying on sub-optimal data is the risk of introducing bias or noise into the learning process. Pseudo-labeling transitions with incorrect rewards could lead to the reward model and RL agent learning incorrect associations, impacting the overall performance. To mitigate this limitation, it is essential to carefully select and preprocess the sub-optimal data to ensure that it accurately represents the task dynamics. Additionally, incorporating mechanisms for error correction and regularization during training can help reduce the impact of noisy data. Regular validation and monitoring of the learning process can also help identify and address any issues arising from the use of sub-optimal data.

Could the ideas behind SDP be applied to other areas of machine learning beyond human-in-the-loop reinforcement learning

The ideas behind SDP, such as leveraging unlabeled sub-optimal data to improve learning efficiency, can be applied to various areas of machine learning beyond human-in-the-loop reinforcement learning. For example, in semi-supervised learning, where labeled data is scarce, leveraging unlabeled data with pseudo-labeling techniques can enhance model performance. Similarly, in transfer learning, utilizing sub-optimal data from related tasks to pre-train models for a target task can expedite learning and improve generalization. The concept of using prior knowledge or imperfect data to guide model training is a versatile approach that can be adapted to different machine learning paradigms to enhance learning efficiency and performance.