Leveraging Offline Data in Online Reinforcement Learning (2211.04974v2)

Abstract: Two central paradigms have emerged in the reinforcement learning (RL) community: online RL and offline RL. In the online RL setting, the agent has no prior knowledge of the environment, and must interact with it in order to find an $\epsilon$-optimal policy. In the offline RL setting, the learner instead has access to a fixed dataset to learn from, but is unable to otherwise interact with the environment, and must obtain the best policy it can from this offline data. Practical scenarios often motivate an intermediate setting: if we have some set of offline data and, in addition, may also interact with the environment, how can we best use the offline data to minimize the number of online interactions necessary to learn an $\epsilon$-optimal policy? In this work, we consider this setting, which we call the \textsf{FineTuneRL} setting, for MDPs with linear structure. We characterize the necessary number of online samples needed in this setting given access to some offline dataset, and develop an algorithm, \textsc{FTPedel}, which is provably optimal, up to $H$ factors. We show through an explicit example that combining offline data with online interactions can lead to a provable improvement over either purely offline or purely online RL. Finally, our results illustrate the distinction between \emph{verifiable} learning, the typical setting considered in online RL, and \emph{unverifiable} learning, the setting often considered in offline RL, and show that there is a formal separation between these regimes.

• The paper introduces FineTuneRL and the Moca algorithm to blend offline data with online exploration, ensuring an ε-optimal policy in linear MDPs.
• The paper establishes novel offline-to-online concentrability coefficients and lower sample complexity bounds that guarantee near-optimal performance.
• The paper highlights practical contrasts between verifiable and unverifiable learning, offering insights for scenarios where online data collection is expensive or risky.

Insights on Leveraging Offline Data in Online Reinforcement Learning

The paper focuses on an intermediate paradigm between purely online and purely offline reinforcement learning (RL). This paradigm, referred to as FineTuneRL, allows a learner to utilize an offline dataset while also interacting with the environment to learn an $\epsilon$-optimal policy. The research distinguishes this approach from existing paradigms by analyzing the sample complexity and developing a novel algorithm called Moca that optimally balances the use of offline and online data for Markov Decision Processes (MDPs) with linear structure.

Key Contributions and Results

1. Offline-to-Online Concentrability: The authors introduce the concept of offline-to-online concentrability coefficient. This coefficient accounts for the level of coverage achieved by combining offline data with additional online samples, expressed as $_{h}(D,\epsilon,T)$. This parameter extends the typical offline concentrability coefficient by incorporating the potential improvement granted by online exploration.
2. Complexity Bounds and Algorithm Development: The paper contributes a lower bound, demonstrating that $\sum_{h=1}^H {^h(D, \epsilon; \beta)}$ online samples are required by any algorithm to guarantee finding an $\epsilon$-optimal policy. To complement this, it introduces Moca, an algorithm that meets this complexity bound, up to lower-order terms and occasional constants regarding the linear MDP dimension $d$ and horizon $H$. Moca's development stands out because it effectively integrates offline data to minimize the requirement for online samples.
3. Performance of Moca: The paper establishes that Moca performs efficiently relative to existing online methods, even surpassing them when offline data is leveraged correctly. The algorithm's performance only incurs an additional term ($\mathcal{O}(1/\epsilon^{8/5}$) associated with data exploration, which highlights Moca's efficiency.
4. Offline vs. Verifiable Learning: A significant distinction made by this research is between verifiable and unverifiable learning within RL contexts. Verifiable learning focuses on ensuring that the returned policy meets a probabilistic performance threshold, whereas unverifiable learning may yield better policies without guarantees. The findings show that optimal verifiable learning requires more stringent conditions on data coverage than unverifiable learning.
5. Theoretical and Practical Implications: The theoretical implications are profound. FineTuneRL provides a robust framework for bridging offline and online RL, guaranteeing that sufficient online exploration complements adequately covered offline data. Practically, this paradigm aligns with the tasks faced in domains where acquiring real-time data is expensive or risky.

Future Directions

The paper outlines several promising directions for future work. These include developing computationally efficient algorithms akin to Moca yet free from current scale limitations related to enumerating policy spaces or $$. Investigating FineTuneRL within general function approximation settings and alternative RL objectives, such as regret minimization, are recommended for extending the theoretical foundations laid by this research.

In conclusion, this paper makes substantial contributions to the landscape of reinforcement learning by blending offline data with online exploration. The creation of Moca and a refined understanding of offline-to-online learning complexities are notable advances that are likely to inspire subsequent research endeavors in the field.