The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Robust Multi-Agent Reinforcement Learning via Minimax Deep Deterministic Policy Gradient
Open accessAbstract
Despite the recent advances of deep reinforcement learning (DRL), agents trained by DRL tend to be brittle and sensitive to the training environment, especially in the multi-agent scenarios. In the multi-agent setting, a DRL agent’s policy can easily get stuck in a poor local optima w.r.t. its training partners – the learned policy may be only locally optimal to other agents’ current policies. In this paper, we focus on the problem of training robust DRL agents with continuous actions in the multi-agent learning setting so that the trained agents can still generalize when its opponents’ policies alter. To tackle this problem, we proposed a new algorithm, MiniMax Multi-agent Deep Deterministic Policy Gradient (M3DDPG) with the following contributions: (1) we introduce a minimax extension of the popular multi-agent deep deterministic policy gradient algorithm (MADDPG), for robust policy learning; (2) since the continuous action space leads to computational intractability in our minimax learning objective, we propose Multi-Agent Adversarial Learning (MAAL) to efficiently solve our proposed formulation. We empirically evaluate our M3DDPG algorithm in four mixed cooperative and competitive multi-agent environments and the agents trained by our method significantly outperforms existing baselines.
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Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512
Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., & Abbeel, P. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202133 · Cited by 3,124 · cited 1×
“This problem is also studied recently with deep neural networks, such as adding random noise to input (Tobin et al. 2017) or dynamics (Peng et al.”
BackgroundA Comprehensive Survey of Multiagent Reinforcement Learning
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Learning to Communicate with Deep Multi-Agent Reinforcement Learning
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Robust adversarial reinforcement learning
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“Besides adding random noise, some other works implicitly adopt the minimax idea by utilizing the “worst noise” (Pinto et al. 2017; Mandlekar et al. 2017).”
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Cited by
Multi-agent deep reinforcement learning: a survey
Gronauer, S., & Diepold, K. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-09996-w · Cited by 957
Transferring policy of deep reinforcement learning from simulation to reality for robotics
Ju, H., Juan, R., Gomez, R., Nakamura, K., & Li, G. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00573-6 · Cited by 147
Metadata
- Authors
- Li, S., Wu, Y., Cui, X., Dong, H., Fang, F., & Russell, S.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 4213-4220
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2904455790
- Cited by
- 290
- References
- 13
- FWCI
- 13.22
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Li, S., Wu, Y., Cui, X., Dong, H., Fang, F., & Russell, S. (2019). Robust Multi-Agent Reinforcement Learning via Minimax Deep Deterministic Policy Gradient. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v33i01.33014213
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