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Abstract:
In this paper, we suggest a novel reinforcement learning architecture, the Natural
Actor-Critic. The actor updates are achieved using stochastic policy gradients em-
ploying Amaris natural gradient approach, while the critic obtains both the natural
policy gradient and additional parameters of a value function simultaneously by lin-
ear regression. We show that actor improvements with natural policy gradients are
particularly appealing as these are independent of coordinate frame of the chosen
policy representation, and can be estimated more efficiently than regular policy gra-
dients. The critic makes use of a special basis function parameterization motivated
by the policy-gradient compatible function approximation. We show that several
well-known reinforcement learning methods such as the original Actor-Critic and
Bradtkes Linear Quadratic Q-Learning are in fact Natural Actor-Critic algorithms.
Empirical evaluations illustrate the effectiveness of our techniques in comparison to
previous methods, and also demonstrate their applicability for learning control on
an anthropomorphic robot arm.
