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About this paper
Zhang-Wei Hong, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee
Researchers at National Tsing Hua University developed Deep Policy Inference Q-Networks (DPIQN and DRPIQN) to enable deep reinforcement learning agents to effectively operate in multi-agent systems. These models learn to infer other agents' policies directly from raw observations and integrate this information into their Q-value estimations, achieving robust performance and faster learning in both competitive and collaborative environments like a soccer game.
Key insights
- Explicitly inferring other agents' policies from raw observations and integrating these 'policy features' into a Q-network improves learning in multi-agent systems.
- An adaptive loss function, which dynamically balances Q-value learning and policy inference, stabilizes training and accelerates convergence.
- Recurrent units (LSTMs) enhance policy inference and overall performance in partially observable multi-agent environments with dynamic agent strategies.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.