Asynchronous Coagent Networks

Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020)

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James Kostas, Chris Nota, Philip Thomas


Coagent policy gradient algorithms (CPGAs) are reinforcement learning algorithms for training a class of stochastic neural networks called coagent networks. In this work, we prove that CPGAs converge to locally optimal policies. Additionally, we extend prior theory to encompass asynchronous and recurrent coagent networks. These extensions facilitate the straightforward design and analysis of hierarchical reinforcement learning algorithms like the option-critic, and eliminate the need for complex derivations of customized learning rules for these algorithms.