Authors: Alexandre Rio, Merwan Barlier, Igor Colin, Albert Thomas

Published on: February 08, 2024

Impact Score: 8.35

Arxiv code: Arxiv:2402.05525

Summary

• What is new: Introduces DP-MORL, a novel MBRL algorithm with differential privacy guarantees for offline reinforcement learning.
• Why this is important: The need to train reinforcement learning policies that protect the privacy of individual data trajectories.
• What the research proposes: Using DP-FedAvg to train a private model of the environment and then applying model-based policy optimization to derive policies without further data access.
• Results: DP-MORL effectively trains private reinforcement learning agents from offline data, highlighting the trade-off between privacy and learning performance.

Technical Details

Technological frameworks used: DP-FedAvg for training neural networks with differential privacy.

Models used: Model-based reinforcement learning (MBRL) for policy optimization.

Data used: Offline data for training without further system interaction.

Potential Impact

Companies in finance, healthcare, and any sector relying on personalized data for AI, offering new ways to protect user privacy while employing reinforcement learning.

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