Heuristic Dynamic Programming (HDP) is the simplest kind of Adaptive Critic which is a powerful form of reinforcement control 1]. It can be used to maximize or minimize any utility function, such as total energy or trajectory error, of a system over time in a noisy environment. Unlike supervised learning, adaptive critic design does not require the desired control signals be known. Instead, fee...

Vibration isolation control is the critical issue to guarantee the performance of various vibration-sensitive instruments and sensors in practical engineering systems. In this paper, single network adaptive critic (SNAC) based controllers are developed for vibration isolation applications. The SNAC approach differs from the typical action-critic dual network structure in adaptive critic designs...

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 ...

We present the first class of policy-gradient algorithms that work with both state-value and policy function-approximation, and are guaranteed to converge under off-policy training. Our solution targets problems in reinforcement learning where the action representation adds to thecurse-of-dimensionality; that is, with continuous or large action sets, thus making it infeasible to estimate state-...

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