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Leveraging Offline Data from Similar Tasks to Improve Batch Reinforcement Learning


Core Concepts
Transferring knowledge from similar source tasks can significantly improve the learning performance of the target reinforcement learning task, even with limited target data.
Abstract
The content discusses a framework for knowledge transfer in batch reinforcement learning (RL) settings, where the goal is to efficiently learn the optimal action-value function (Q*) by leveraging data from similar source tasks. Key highlights: The authors formally define task discrepancy between the target and source RL tasks based on differences in reward functions and transition probabilities. They propose a general Transferred Fitted Q-Iteration (TransFQI) algorithm that iteratively estimates Q* by combining a center estimator (learned from all tasks) with a bias-correction estimator (learned for each task). The theoretical analysis shows that with mild task discrepancy and sufficient source data, the target task can enjoy a significantly improved convergence rate compared to single-task RL estimation. The authors instantiate the general framework using sieve function approximation and provide detailed theoretical guarantees. Empirical results on synthetic and real datasets demonstrate the effectiveness of the proposed transfer learning approach in batch RL settings.
Stats
The reward functions r(k)(x, a) are uniformly upper bounded by a constant Rmax. The noise η(k) i,t in the reward is σ2 η-sub-Gaussian.
Quotes
"The goal of this paper is to study knowledge transfer under posterior shifts in learning the Q∗function from offline datasets." "Lemma 2.1 shows that the magnitude of the difference of Q∗functions can be upper bounded by that of δr and δρ, which theoretically guarantees the transferability across RL tasks that are similar in reward functions and transition kernels for optimal Q∗learning."

Key Insights Distilled From

by Elynn Chen,X... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.15209.pdf
Data-Driven Knowledge Transfer in Batch $Q^*$ Learning

Deeper Inquiries

How can the proposed transfer learning framework be extended to handle non-stationary or partially observable Markov decision processes

To extend the proposed transfer learning framework to handle non-stationary or partially observable Markov decision processes (MDPs), several modifications and considerations need to be made. Non-Stationary MDPs: In non-stationary MDPs, the transition probabilities and reward functions may change over time. To address this, the framework can be adapted to incorporate time-varying parameters or models. This could involve updating the estimation process to account for changes in the environment over time. Techniques such as online learning or adaptive algorithms can be employed to continuously update the Q-values based on new data and evolving dynamics. Partially Observable MDPs: In partially observable MDPs, the agent does not have full observability of the state. To handle this, the framework can be extended to include techniques from Partially Observable Markov Decision Processes (POMDPs). This may involve incorporating belief states or hidden state variables to account for uncertainty in observations. Additionally, advanced exploration strategies or information-gathering policies can be implemented to improve decision-making in partially observable environments. Hybrid Models: For scenarios where both non-stationarity and partial observability are present, a hybrid approach combining techniques from non-stationary MDPs and POMDPs can be utilized. This may involve integrating adaptive learning algorithms with belief state estimation to navigate complex and changing environments effectively. By incorporating these adaptations and techniques, the transfer learning framework can be extended to address the challenges posed by non-stationary and partially observable MDPs, enabling more robust and adaptive decision-making in dynamic and uncertain environments.

What are the potential challenges and limitations of applying the TransFQI algorithm in high-dimensional or continuous state-action spaces

The Transferred Fitted Q-Iteration (TransFQI) algorithm, while effective in handling transfer learning in batch stationary environments, may face challenges and limitations when applied to high-dimensional or continuous state-action spaces. Curse of Dimensionality: In high-dimensional spaces, the number of parameters or basis functions required to accurately represent the Q-function may increase exponentially, leading to computational complexity and overfitting. This can result in difficulties in convergence and generalization, especially when using function approximation methods like sieve approximation. Function Approximation Errors: In continuous state-action spaces, the approximation errors introduced by function approximators such as neural networks or spline bases can impact the accuracy of the Q-value estimates. The algorithm may struggle to capture the intricate relationships between states and actions, leading to suboptimal policy learning. Exploration-Exploitation Trade-off: In high-dimensional spaces, exploration becomes more challenging as the agent needs to efficiently explore a vast state-action space to discover optimal policies. Balancing exploration and exploitation becomes crucial, and the algorithm may face difficulties in effectively exploring the environment to learn optimal policies. To mitigate these challenges, techniques such as advanced function approximation methods, regularization strategies, and sophisticated exploration policies can be incorporated into the TransFQI algorithm. Additionally, dimensionality reduction techniques or feature engineering approaches can help simplify the state-action space and improve the algorithm's performance in high-dimensional settings.

Can the theoretical insights derived in this work be leveraged to design more efficient exploration strategies for transfer learning in online RL settings

The theoretical insights derived from the Transferred Fitted Q-Iteration (TransFQI) algorithm can indeed be leveraged to design more efficient exploration strategies for transfer learning in online Reinforcement Learning (RL) settings. Task Discrepancy Analysis: The theoretical analysis in the work sheds light on the impact of task discrepancies on the effectiveness of knowledge transfer. By understanding how differences between tasks affect the learning process, more informed exploration strategies can be designed to adapt to varying task complexities and similarities. Commonality Estimation: The insights on commonality estimation error provide guidance on how to identify and leverage shared components across tasks for efficient transfer learning. By focusing exploration efforts on common features or structures, agents can expedite learning and improve decision-making in online RL scenarios. Algorithmic Error Reduction: The theoretical guarantees on algorithmic error highlight the importance of minimizing estimation biases and errors during the learning process. By optimizing the algorithmic components and reducing error rates, exploration strategies can be enhanced to facilitate faster convergence and improved performance in online RL settings. By incorporating these theoretical insights into the design of exploration strategies, practitioners can develop more efficient and adaptive approaches for transfer learning in online RL, leading to enhanced learning capabilities and better decision-making in dynamic environments.
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