Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning (2201.13425v3)

Abstract: Recent progress in deep learning has relied on access to large and diverse datasets. Such data-driven progress has been less evident in offline reinforcement learning (RL), because offline RL data is usually collected to optimize specific target tasks limiting the data's diversity. In this work, we propose Exploratory data for Offline RL (ExORL), a data-centric approach to offline RL. ExORL first generates data with unsupervised reward-free exploration, then relabels this data with a downstream reward before training a policy with offline RL. We find that exploratory data allows vanilla off-policy RL algorithms, without any offline-specific modifications, to outperform or match state-of-the-art offline RL algorithms on downstream tasks. Our findings suggest that data generation is as important as algorithmic advances for offline RL and hence requires careful consideration from the community. Code and data can be found at https://github.com/denisyarats/exorl .

• The paper introduces a data-centric approach that replaces algorithm tweaks with unsupervised exploratory data collection to improve offline RL outcomes.
• The methodology demonstrates that vanilla RL algorithms can outperform complex offline-specific methods when trained on diverse, relabeled datasets.
• Experimental results reveal that exploratory data supports robust multi-task learning and effectively mitigates challenges like extrapolation error.

Exploratory Data for Offline Reinforcement Learning

The paper "Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning" proposes a data-centric approach to tackle the challenges of offline reinforcement learning (RL). This research pivots the focus from algorithmic modifications to data enhancements, specifically through the lens of unsupervised reward-free exploration and its implications in offline RL.

Background and Motivation

Offline RL has seen less explosive growth compared to other domains of machine learning due to its reliance on datasets collected from task-specific policies, which limits data diversity. This restriction implies that the potential of large and varied datasets, which have been instrumental in the success of areas like computer vision and natural language processing, is underexplored in offline RL contexts.

Exploratory Data for Offline RL (E)

The framework introduced, termed as Exploratory data for Offline RL (E), comprises three main stages:

1. Data Collection: Unsophisticated exploration methods generate a broad-range dataset without rewards.
2. Data Relabeling: The generated dataset is later annotated with various downstream reward functions.
3. Offline Learning: Standard offline RL algorithms are employed on the labeled datasets to develop policies.

A key innovation presented is collecting diverse datasets using unsupervised strategies. This contrasts with the conventional approach of using online data or datasets optimized for specific tasks, as seen in benchmarks like D4RL and RL Unplugged.

Experimental Results

The paper meticulously evaluates the ability of existing offline RL algorithms when trained on diverse datasets. Several crucial insights emerge:

• Performance of Vanilla RL Algorithms: With sufficiently diverse exploratory data, vanilla off-policy RL algorithms like TD3 often outperform more complex offline-specific algorithms (CRR, CQL, TD3+BC) on certain environments. This highlights that the diversity and coverage of data can mitigate issues like extrapolation error, typically a significant challenge in offline settings.
• Multi-task RL Capabilities: The capacity for data collected via exploratory means to be repurposed across multiple reward functions is underscored, suggesting versatility and efficiency in offline RL applications not seen with task-specific datasets.
• Necessity of Exploratory Data: The ability of unsupervised exploratory data to generalize across tasks, where supervised data fails, is a notable finding. It suggests that diversity, rather than specificity, in data collection can enhance multi-task learning capabilities within offline RL frameworks.

Implications and Future Directions

This paper emphasizes the importance of dataset design in offline RL, questioning the primacy given to algorithmic innovations thus far. In practice, this method suggests that strategically collected and diverse exploratory datasets can produce superior generalization in RL tasks. Furthermore, this work lays the foundation for developing benchmarks that test the adaptability and robustness of offline RL methods beyond traditional constraints.

Theoretical Implications: The findings advocate for a reevaluation of core assumptions in offline RL, particularly the role of task-specific data. It encourages further research into exploration strategies that prioritize diverse data coverage.

Practical Implications: This paper guides practitioners towards incorporating exploratory data collection strategies in RL pipelines, potentially enriching the toolkit available for real-world reinforcement learning problems where exploration is costly or limited.

Future Work: The paper suggests several avenues for follow-up work, including the refinement of unsupervised exploration strategies to enhance their efficacy across more complex environments, as well as developing algorithms that inherently adapt to the characteristics of the datasets they utilize.

In conclusion, this research offers a paradigm shift in offline RL by demonstrating that with strategic and diverse data collection, traditional RL methodologies can achieve significant results without the need for intricate algorithmic adjustments tailored for the offline paradigm.