Privacy Preserving Reinforcement Learning with One-Sided Feedback
Learn how to implement privacy-preserving reinforcement learning with one-sided feedback using the POOL algorithm, which addresses challenges in learning efficiency and privacy preservation.
- Implement the POOL algorithm to handle one-sided feedback in RL environments
- Configure the POOL algorithm to balance learning efficiency and privacy preservation
- Test the POOL algorithm on a multi-dimensional continuous state and action space
- Compare the performance of POOL with other RL algorithms on privacy preservation and learning efficiency
- Apply the POOL algorithm to real-world applications, such as autonomous systems or personalized recommendation engines
This research benefits RL researchers and engineers working on privacy-preserving ML, particularly those in teams focused on developing autonomous systems or personalized recommendation engines, as it provides a novel approach to handling one-sided feedback in multi-dimensional continuous state and action spaces.
💡 POOL addresses the challenges of learning efficiency and privacy preservation in RL with one-sided feedback, providing a promising approach for autonomous systems and personalized recommendation engines.
🤖 Introducing POOL, a novel privacy-preserving RL algorithm for one-sided feedback in multi-dimensional continuous state and action spaces! 📊
Key Takeaways
Learn how to implement privacy-preserving reinforcement learning with one-sided feedback using the POOL algorithm, which addresses challenges in learning efficiency and privacy preservation.
Full Article
Abstract:
arXiv:2605.18246v1 Announce Type: cross Abstract: We study reinforcement learning (RL) in multi-dimensional continuous state and action spaces with one-sided feedback, where the agent receives partial observations of the state and obtains reward information for only a subset of the state-action space at each time step. This setting introduces substantial challenges in both learning efficiency and privacy preservation. To address these challenges, we propose POOL, a novel privacy-preserving RL al
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