Natural actor criticIncremental learningImplicit updateNatural policy gradient (NPG) methods are promising approaches to finding locally optimal policy parameters. The NPG approach works well in optimizing comp
Incremental natural actor-critic algorithms - Bhatnagar, Sutton, et al. - 2008 () Citation Context ...y the value of θ greedily in this direction. Natural gradient methods do not follow the steepest direction in parameter space, but rather the steepest direction with respect to the Fisher ...
There are relatively few studies that explore reinforcement learning for myoelectric control with a reward signal that directly quantifies functional performance from a user-in-the-loop setting. Pilarski et al. has demonstrated that actor critic reinforcement learning can optimize the control of a robo...
Prominent algorithms in this category are actor-critic (Konda and Tsitsiklis 2003), policy gradient (Baxter and Bartlett 2001), natural actor-critic (Bhatnagar et al. 2009) and fast policy search (Mannor et al. 2003). Indirect methods are based on the certainty equivalence of computing where ...
Natural policy gradientIncre-mental natural actor criticIncremental learningImplicit updateThe natural policy gradient (NPG) method is a promising approach to find a locally optimal policy parameter. The NPG method has been demonstrated remarkable successes in many fields, including the large scale ...
The improvement in learning evaluation efficiency of the Critic will contribute to the improvement in policy learning performance of the Actor. Simulation results on the learning control of an inverted pendulum and a mountain-car problem illustrate the effectiveness of the two proposed AC algorithms in...
algorithm. The Critic estimates the value-function according to the iLSTD(λ) algorithm, and the Actor updates the policy parameter based on a regular gradient. Simulation results concerning a grid world with 10×10 size illustrate that the AC algorithm based on iLSTD(λ) not only has quick ...