Bayesian role discovery for multi-agent reinforcement learning
By Aaron Wilson, Alan Fern, Prasad Tadepalli. 2010
9 citations recorded by the source corpus.
In this paper we develop a Bayesian policy search approach for Multi-Agent RL (MARL), which is model-free and allows for priors on policy parameters. We present a novel optimization algorithm based on hybrid MCMC, which leverages both the prior and gradient information estimated from trajectories. Our experiments demonstrate the automatic discovery of roles through reinforcement learning in a real-time strategy game.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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In Artificial Intelligence
In Computer Vision and Pattern Recognition
Citations in the corpus
- Multiagent Learning: Basics, Challenges, and Prospects (2012, background): “Currently very little multiagent transfer learning work is available (for example, Ammar and Taylor [2011], Proper and Tadepalli [2009], Wilson et al. [2008], Wilson, Fern, and Tadepalli [2010]).”