The Generalization Gap in Offline Reinforcement Learning (2312.05742v2)

Abstract: Despite recent progress in offline learning, these methods are still trained and tested on the same environment. In this paper, we compare the generalization abilities of widely used online and offline learning methods such as online reinforcement learning (RL), offline RL, sequence modeling, and behavioral cloning. Our experiments show that offline learning algorithms perform worse on new environments than online learning ones. We also introduce the first benchmark for evaluating generalization in offline learning, collecting datasets of varying sizes and skill-levels from Procgen (2D video games) and WebShop (e-commerce websites). The datasets contain trajectories for a limited number of game levels or natural language instructions and at test time, the agent has to generalize to new levels or instructions. Our experiments reveal that existing offline learning algorithms struggle to match the performance of online RL on both train and test environments. Behavioral cloning is a strong baseline, outperforming state-of-the-art offline RL and sequence modeling approaches when trained on data from multiple environments and tested on new ones. Finally, we find that increasing the diversity of the data, rather than its size, improves performance on new environments for all offline learning algorithms. Our study demonstrates the limited generalization of current offline learning algorithms highlighting the need for more research in this area.

• The paper introduces a novel benchmark using Procgen and WebShop scenarios to assess offline RL generalization.
• The paper shows that offline RL methods often underperform compared to online approaches, with behavioral cloning emerging as a competitive baseline.
• The paper demonstrates that increasing training data diversity, rather than volume, significantly improves performance in novel test conditions.

Generalization in Offline Reinforcement Learning

Introduction to the Study

Offline reinforcement learning (RL) presents significant gains because it allows agents to learn from pre-collected static datasets without the need for real-time interactions with the environment. This approach is particularly useful in areas where gathering new data can be costly or dangerous, such as in healthcare or autonomous driving. However, the effectiveness of offline RL algorithms in adapting to novel scenarios remains less understood, particularly compared to online RL methods that learn through active interaction with their environment.

Generalization Benchmarks and Findings

The research introduces a novel benchmark featuring two distinct scenarios for assessing the generalization performance of offline RL. The first scenario involves unseen levels within Procgen, a series of 2D video games, while the second evaluates performance on new natural language instructions within WebShop, an e-commerce environment.

The studies' findings expose a significant challenge for existing offline RL methods. When compared to online RL, these algorithms generally underperform in environments that are different from their training conditions, even if trained on high-quality expert data. Behavioral cloning (BC), a simpler approach relying solely on mimicking observed behaviors without complex policy optimization, proves to be one of the most competitive baselines, frequently outpacing more sophisticated offline RL methods.

Data Diversity Enhances Generalization

One striking discovery is the substantial impact of data diversity on algorithm performance. Unlike the common assumption that having more data leads to improved performance, findings suggest that the quality and variety of training data play a more crucial role. Specifically, increasing the diversity of training environments while keeping the total size of the dataset fixed leads to better outcomes when dealing with novel environments during testing.

Concluding Insights

The paper highlights the need for continued research dedicated to enhancing offline RL methods' generalizability. The current limitations of such algorithms when faced with scenarios different from their training data point towards a potential recalibration or even a revolution in approach. Future work could explore integrating methods used in online RL for improving generalization or developing new algorithms attuned to the demands of learning from static, diverse datasets.

Forward Look

With the open-sourced benchmarks and baselines provided by this paper, the research community is better equipped to lower barriers to entry and incentivize exploration into the generalization capabilities of offline RL. By directing attention to the importance of training data diversity, this work hopes to inspire more robust and versatile algorithms capable of stepping away from the simulated training grounds and into the complex real-world applications they are destined for.