Policy gradient methods

Peters, J

doi:10.4249/scholarpedia.3698

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Policy gradient methods

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Peters, J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Peters, J. (2010). Policy gradient methods. Scholarpedia, 5(11), 3698. doi:10.4249/scholarpedia.3698.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-BD68-3

Abstract

Policy gradient methods are a type of reinforcement learning techniques that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by gradient descent. They do not suffer from many of the problems that have been marring traditional reinforcement learning approaches such as the lack of guarantees of a value function, the intractability problem resulting from uncertain state information and the complexity arising from continuous states actions.