2011
NIPS
NeurIPS 2011
Policy Gradient Coagent Networks
Abstract
We present a novel class of actor-critic algorithms for actors consisting of sets of interacting modules. We present, analyze theoretically, and empirically evaluate an update rule for each module, which requires only local information: the module's input, output, and the TD error broadcast by a critic. Such updates are necessary when computation of compatible features becomes prohibitively difficult and are also desirable to increase the biological plausibility of reinforcement learning methods.
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Interdisciplinary Bridge
- Artificial Intelligence and Machine Learning and Reinforcement Learning
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Trend Setter
- Multi-Agent Systems
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Keyword Pioneer
- coagent networks
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Hot Topic Early Bird
- reinforcement learning
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Cross-Pollinator
- Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics