We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical contribution, we clarify the situation with bias in GAN loss functions raised by recent work: we show that gradient estimators used in the optimization process for both MMD GANs and Wasserstein GANs are unbiased, but learning...

Pretraining with expert demonstrations have been found useful in speeding up the training process of deep reinforcement learning algorithms since less online simulation data is required. Some people use supervised learning to speed up the process of feature learning, others pretrain the policies by imitating expert demonstrations. However, these methods are unstable and not suitable for actor-c...

A self tuning PID control strategy using reinforcement learning is proposed in this paper to deal with the control of wind energy conversion systems (WECS). Actor-Critic learning is used to tune PID parameters in an adaptive way by taking advantage of the model-free and on-line learning properties of reinforcement learning effectively. In order to reduce the demand of storage space and to impro...

Since 1995, numerous Actor-Critic architectures for reinforcement learning have been proposed as models of dopamine-like reinforcement learning mechanisms in the rat’s basal ganglia. However, these models were usually tested in different tasks, and it is then difficult to compare their efficiency for an autonomous animat. We present here the comparison of four architectures in an animat as it p...

Neurocontrol is a crucial area of fundamental research within the neural network eld. Adaptive Heuristic Critic learning is a key algorithm for real time adaptation in neurocontrollers. In this paper we present how an unsupervised neural network model with adaptable structure can be used to speed-up Adaptive Heuristic Critic learning, its FPGA design , and how it adapts the neurocontroller to t...

We present a novel class of actor-critic algorithms for actors consisting of sets of interacting modules. We present, analyze theoretically, and empirically evaluate an update rule for each module, which requires only local information: the module’s input, output, and the TD error broadcast by a critic. Such updates are necessary when computation of compatible features becomes prohibitively dif...

We present four new reinforcement learning algorithms based on actor-critic and natural-gradient ideas, and provide their convergence proofs. Actor-critic reinforcement learning methods are online approximations to policy iteration in which the value-function parameters are estimated using temporal difference learning and the policy parameters are updated by stochastic gradient descent. Methods...

Actor-Critic architectures of reinforcement learning were found to show a strong resemblance with known anatomy and function of a part of the vertebrate's brain: the basal ganglia. Based on this analogy, a large number of Actor-Critic models were simulated to reproduce behaviours of rats performing laboratory tasks. However, most of these models were tested in different tasks and it is often di...

In this article, we propose an architecture of a bio-inspired controller that addresses the problem of learning different locomotion gaits for different robot morphologies. The modeling objective is split into two: baseline motion modeling and dynamics adaptation. Baseline motion modeling aims to achieve fundamental functions of a certain type of locomotion and dynamics adaptation provides a "r...

parapsychology, in the eighteenth and nineteenth centuries, have considered, by physicists and philosophers,by institutes which formed ,and in the twentieth century, especially during the post-modern age, as means of critic and break the materialistic and physicalistic view of modernity, is more important. review this subject ,in the islamic culture,breasting our with  researches  of avesina th...