Search Results for author: Qizhen Zhang

Found 3 papers, 1 papers with code

Analysing the Sample Complexity of Opponent Shaping

no code implementations8 Feb 2024 Kitty Fung, Qizhen Zhang, Chris Lu, Jia Wan, Timon Willi, Jakob Foerster

Providing theoretical guarantees for M-FOS is hard because A) there is little literature on theoretical sample complexity bounds for meta-reinforcement learning B) M-FOS operates in continuous state and action spaces, so theoretical analysis is challenging.

Meta Reinforcement Learning

Centralized Model and Exploration Policy for Multi-Agent RL

1 code implementation14 Jul 2021 Qizhen Zhang, Chris Lu, Animesh Garg, Jakob Foerster

We also learn a centralized exploration policy within our model that learns to collect additional data in state-action regions with high model uncertainty.

Reinforcement Learning (RL)

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