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Zero-Shot Reinforcement Learning from Low Quality Data (2309.15178v3)

Published 26 Sep 2023 in cs.LG and cs.AI

Abstract: 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. Methods leveraging successor measures and successor features have shown strong performance in this setting, but require access to large heterogenous datasets for pre-training which cannot be expected for most real problems. Here, we explore how the performance of zero-shot RL methods degrades when trained on small homogeneous datasets, and propose fixes inspired by conservatism, a well-established feature of performant single-task offline RL algorithms. We evaluate our proposals across various datasets, domains and tasks, and show that conservative zero-shot RL algorithms outperform their non-conservative counterparts on low quality datasets, and perform no worse on high quality datasets. Somewhat surprisingly, our proposals also outperform baselines that get to see the task during training. Our code is available via https://enjeeneer.io/projects/zero-shot-rl/ .

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Summary

  • The paper introduces conservative regularization techniques, VC-FB and MC-FB, to mitigate value overestimation issues in zero-shot RL trained on low-quality datasets.
  • Empirical validation demonstrates that these conservative methods improve zero-shot performance by up to 1.5 imes on low-quality data and match task-specific baselines like CQL.
  • The findings suggest that these techniques enable more robust deployment of zero-shot RL in real-world applications facing data scarcity or low quality without performance loss on good data.

Insightful Overview of "Zero-Shot Reinforcement Learning from Low Quality Data"

The paper "Zero-Shot Reinforcement Learning from Low Quality Data" tackles a significant challenge in the field of zero-shot reinforcement learning (RL): the effective utilization of low-quality or homogeneous datasets for pre-training without rewards. Addressing the practical constraints faced in real-world deployments, this research proposes methodologies grounded in conservatism—a noted success factor in single-task offline RL—to enhance zero-shot learning performance.

Core Contributions and Methodology:

The authors investigate the inherent limitations of existing zero-shot RL methods when trained on narrow datasets which lack diversity. Specifically, these methods tend to suffer from the well-documented issue of out-of-distribution (OOD) state-action value overestimation. This exploration leads to the development of conservative regularization techniques tailored for the zero-shot setting, intended to mitigate this overestimation.

  1. Conservative Regularization: The paper introduces two primary algorithms: Value-Conservative Forward-Backward Representations (VC-FB) and Measure-Conservative Forward-Backward Representations (MC-FB). These algorithms are designed to suppress the predicted values of OOD actions across all tasks, employing a regularization term similar in essence to conservative Q-learning (CQL). This regularization operates on the successor measures and features foundational to the FB framework.
  2. Empirical Validation: Through experimentation across various environments—including locomotion tasks such as Walker and Quadruped, and goal-oriented tasks like Point-mass Maze—the authors establish that conservative regularization can improve zero-shot RL performance. Notably, VC-FB and MC-FB demonstrate up to a 1.5× improvement over non-conservative counterparts when tested on low-quality datasets. Moreover, they achieve performance levels on par with task-specific baselines such as CQL, which directly benefit from access to task-specific reward labels.
  3. Scalability: Importantly, the paper shows that incorporating conservatism does not degrade the effectiveness of zero-shot RL methods even when ample, high-quality data is available. This suggests that the conservative approach proposed adds robustness against data scarcity and low-quality training scenarios without a trade-off in larger datasets.

Theoretical and Practical Implications:

  • Theoretical Advancement: The integration of conservative principles into the zero-shot framework opens new avenues for further methodological enhancements. By focusing on value and measure suppression across task vectors, the research contributes to the theoretical understanding of RL's adaptability to suboptimal pre-training conditions.
  • Practical Deployment: The findings suggest a potential path forward for deploying zero-shot RL systems in real-world applications where curated, heterogeneous datasets are often infeasible due to cost or risk. Industries such as robotics and autonomous systems, where direct exploration may be limited, can benefit significantly from these methods.

Future Directions in AI:

This work sets a foundational precedent for integrating sophisticated regularization techniques into general-purpose RL algorithms. Future research could extend these findings by exploring adaptive conservatism that dynamically balances exploration and exploitation based on dataset characteristics. This could lead to more resilient AI systems capable of operating across a broader spectrum of real-world settings, where data quality and availability are variable.

In conclusion, the paper provides a detailed investigation into the problematic field of zero-shot learning under low-quality data constraints and suggests robust methodological advancements. These techniques not only bridge the gap between theoretical RL models and practical deployments but also lay the groundwork for further exploration into efficient, data-sufficient learning paradigms in the AI community.

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