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Abstract:
In this paper, we investigate motor primitive learning with the
Natural Actor-Critic approach. The Natural Actor-Critic consists out of
actor updates which are achieved using natural stochastic policy gradients
while the critic obtains the natural policy gradient by linear regression.
We show that this architecture can be used to learn the building blocks
of movement generation, called motor primitives. Motor primitives are
parameterized control policies such as splines or nonlinear differential equations
with desired attractor properties. We show that our most modern
algorithm, the Episodic Natural Actor-Critic outperforms previous algorithms by at least an order of magnitude. We demonstrate the efficiency of this reinforcement learning method in the application of learning to hit a baseball with an anthropomorphic robot arm.
