Improving Zero-Shot Reinforcement Learning Performance on Low-Quality Datasets

Główne pojęcia

Existing zero-shot reinforcement learning methods suffer performance degradation when trained on small, homogeneous datasets due to out-of-distribution action value overestimation. Introducing conservative regularization can mitigate this issue and improve performance on low-quality datasets without sacrificing performance on high-quality datasets.

Streszczenie

The paper explores the problem of zero-shot reinforcement learning (RL) from low-quality datasets, where the dataset is small and homogeneous, unlike the large, diverse datasets assumed in prior work.
Key highlights:

Existing zero-shot RL methods, such as forward-backward (FB) representations and universal successor features (USFs), suffer performance degradation on low-quality datasets due to out-of-distribution (OOD) action value overestimation.
The authors propose conservative variants of these methods, called value-conservative FB (VC-FB) and measure-conservative FB (MC-FB), which regularize the value or measure predictions for OOD actions.
Experiments on the ExORL benchmark show that the conservative variants outperform their non-conservative counterparts by up to 1.5x in aggregate performance, and even surpass the performance of task-specific baselines that have access to reward labels.
The conservative variants maintain performance on large, diverse datasets, suggesting they can be adopted without significant downside.
The authors also provide a didactic example illustrating how conservatism can help synthesize the necessary information from a limited dataset to solve multiple tasks.
Overall, the paper presents a step towards enabling the real-world deployment of zero-shot RL methods by addressing their sensitivity to low-quality datasets.

Statystyki

"The largest gap in performance between the conservative FB variants and FB is on the RND dataset."
"The aggregate relative performance of each method is as expected i.e. DVC-FB < MC-FB < VC-FB."
"The conservative variants maintain performance on large, diverse datasets, suggesting they can be adopted without significant downside."

Cytaty

"Zero-shot reinforcement learning (RL) promises to provide agents that can perform any task in an environment after an offline, reward-free pre-training phase."
"Can we still perform zero-shot RL using these datasets? This is the primary question this paper seeks to answer."
"Somewhat surprisingly, our proposals also outperform baselines that get to see the task during training."

Kluczowe wnioski z

Zero-Shot Reinforcement Learning from Low Quality Data

by Scott Jeen,T... o arxiv.org 04-18-2024

https://arxiv.org/pdf/2309.15178.pdf

Głębsze pytania

How can the proposed conservative regularization be extended to other zero-shot RL methods beyond FB and USFs

The proposed conservative regularization can be extended to other zero-shot RL methods beyond FB and USFs by adapting the regularization techniques to suit the specific characteristics of each method. For example, for methods that rely on successor features, a similar approach could be taken to regularize the successor features to suppress the values of out-of-distribution actions. This could involve modifying the loss function to penalize high values for actions that are not well-represented in the dataset. By incorporating a form of conservatism into the training process of other zero-shot RL methods, it may help improve their performance on low-quality datasets and enhance generalization to unseen tasks.

What are the theoretical guarantees or bounds on the performance improvement of the conservative variants compared to their non-conservative counterparts

Theoretical guarantees or bounds on the performance improvement of the conservative variants compared to their non-conservative counterparts can be analyzed through formal analysis and empirical evaluation. The performance improvement can be theoretically justified by considering the impact of the regularization on mitigating the overestimation of out-of-distribution actions, which can lead to more stable and reliable policy learning. By constraining the values of actions that are not well-represented in the dataset, the conservative variants can prevent the model from making overly optimistic estimates and improve the robustness of the learned policies. Empirical evaluation on various datasets and tasks can provide insights into the effectiveness of the conservative regularization in practice and validate the theoretical expectations.

Can the insights from this work be applied to improve the sample efficiency of zero-shot RL methods when transitioning from offline to online interaction with the environment

The insights from this work can be applied to improve the sample efficiency of zero-shot RL methods when transitioning from offline to online interaction with the environment by leveraging the conservative regularization techniques to guide exploration and policy learning. By incorporating conservatism into the training process, the agent can learn more robust policies that are less likely to overestimate the value of out-of-distribution actions, leading to more efficient exploration and better generalization to unseen tasks. This can help the agent adapt more effectively to the online environment by reducing the risk of taking suboptimal actions based on inaccurate value estimates. Additionally, the insights from this work can inform the design of online learning algorithms that prioritize exploration in regions where the model has high uncertainty or lacks sufficient data, improving the overall sample efficiency of the learning process.