Provably Efficient Contrastive Self-Supervised Learning for Online Reinforcement Learning

Core Concepts

Contrastive self-supervised learning can provably recover the underlying true transition dynamics, enabling efficient exploration in reinforcement learning.

Abstract

The paper proposes a reinforcement learning algorithm that integrates contrastive self-supervised learning for representation learning. The key highlights are:

The algorithm learns low-dimensional representations of states and actions by minimizing a contrastive loss, which is shown to recover the true transition dynamics under the low-rank MDP assumption.

The learned representations are then used to construct an upper confidence bound (UCB) bonus term, which enables efficient exploration in the online RL setting.

Theoretical analysis is provided to show that the proposed algorithm achieves a sample complexity of Õ(1/ε^2) for attaining an ε-approximate optimal policy in MDPs.

The algorithm and theory are further extended to the zero-sum Markov game setting, where the representations are used to construct upper and lower confidence bounds (ULCB) for efficient exploration.

Empirical studies are conducted to demonstrate the efficacy of the UCB-based contrastive learning method for reinforcement learning.

Stats

The paper does not provide any specific numerical data or statistics. The theoretical analysis focuses on establishing sample complexity bounds for the proposed algorithms.

Quotes

None.

Key Insights Distilled From

Contrastive UCB

by Shuang Qiu,L... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2207.14800.pdf

Deeper Inquiries

How can the proposed contrastive learning approach be extended to settings with unknown reward functions

The proposed contrastive learning approach can be extended to settings with unknown reward functions by incorporating reward estimation techniques. One possible approach is to use function approximation methods to estimate the unknown reward function based on the observed data. This estimated reward function can then be used in the reinforcement learning framework along with the contrastive learning for representation learning. By iteratively updating the reward estimation and the learned representations, the algorithm can adapt to the unknown reward setting and improve the policy learning process.

Can the analysis be generalized to handle function approximation errors in the representation learning step

The analysis can be generalized to handle function approximation errors in the representation learning step by incorporating regularization techniques and error bounds into the theoretical framework. By considering the errors introduced by function approximation methods, the analysis can provide guarantees on the accuracy of the learned representations despite the approximation errors. Techniques such as regularization terms in the loss function and error analysis in the optimization process can help account for function approximation errors and ensure the robustness of the contrastive learning approach.

What are the potential applications of the contrastive RL framework in real-world decision-making problems, such as healthcare or autonomous driving

The contrastive RL framework has the potential for various applications in real-world decision-making problems, such as healthcare and autonomous driving. In healthcare, the framework can be used for patient treatment optimization, disease diagnosis, and personalized medicine. By learning effective representations of patient data through contrastive learning, healthcare providers can make better decisions and improve patient outcomes. In autonomous driving, the framework can enhance decision-making processes for self-driving vehicles by learning representations of complex driving scenarios and environments. This can lead to safer and more efficient autonomous driving systems. Overall, the contrastive RL framework has the potential to revolutionize decision-making in various real-world applications.