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Found 4 papers, 2 papers with code
Large Model Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends
Large Model (LM) agents, powered by large foundation models such as GPT-4 and DALL-E 2, represent a significant step towards achieving Artificial General Intelligence (AGI).
SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems
Furthermore, we evaluate three potential defenses aimed at exploring ways to mitigate security threats posed by adversarial policies, providing constructive recommendations for deploying MARL in competitive environments.
ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning
In safety-critical domains such as autonomous vehicles, offline deep reinforcement learning (offline DRL) is frequently used to train models on pre-collected datasets, as opposed to training these models by interacting with the real-world environment as the online DRL.
Privacy-preserving Distributed Machine Learning via Local Randomization and ADMM Perturbation
With the proliferation of training data, distributed machine learning (DML) is becoming more competent for large-scale learning tasks.
