Compatible Value Gradients for Reinforcement Learning of Continuous Deep Policies

David Balduzzi; Muhammad Ghifary

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arXiv · 1509.03005

Compatible Value Gradients for Reinforcement Learning of Continuous Deep Policies

David Balduzzi·Muhammad Ghifary

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Abstract

This paper proposes GProp, a deep reinforcement learning algorithm for continuous policies with compatible function approximation. The algorithm is based on two innovations. Firstly, we present a temporal-difference based method for learning the gradient of the value-function. Secondly, we present the deviator-actor-critic (DAC) model, which comprises three neural networks that estimate the value function, its gradient, and determine the actor's policy respectively. We evaluate GProp on two challenging tasks: a contextual bandit problem constructed from nonparametric regression datasets that is designed to probe the ability of reinforcement learning algorithms to accurately estimate gradients; and the octopus arm, a challenging reinforcement learning benchmark. GProp is competitive with fully supervised methods on the bandit task and achieves the best performance to date on the octopus arm.

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BibTeX RIS

David Balduzzi, Muhammad Ghifary. 2015-09-10. Compatible Value Gradients for Reinforcement Learning of Continuous Deep Policies. https://arxiv.org/abs/1509.03005

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