… ARCHITECTURES WITH Q-LEARNING We consider the task of training a learning agent to
sequentially choose neural network layers. Figure 2 shows feasible state and action spaces (…

… There are many reinforcement learning tasks where the … it has been used as a reinforcement
learning benchmark for over 30 … by a hinge and the neural network must apply force to the …

… Reinforcement learning agents are adaptive, reactive, and self-supervised. The aim of this …
art of reinforcement learning and enable its applications to complex robot-learning problems. …

… We present a method to provide transparency for Deep Neural Networks, called object …
Since the Q-values are approximated by a deep neural networks, the Q-value function Q(s,a…

… learning, deep learning and reinforcement learning. Next we discuss core RL elements,
including value function, in particular, Deep Q-Network (… when we use deep neural networks to …

… In the following section, we will address some unsupervised deep neural networks which
are used in order to learn compact low-dimensional feature space of the RL task. Solving visual…

… To learn several skills at the same time, we propose to use Stochastic Neural Networks (SNNs),
a general class of neural networks with stochastic units in the computation graph. There …

… In order to validate EANT, it was used with a complete simulation of a visual servoing scenario
to learn neural networks by reinforcement learning. The same task was given to NEAT [26]…

… —artificial neural networks and reinforcement learning—can … of feedforward neural networks
in reinforcement learning applied to … learnt to swim thanks to feedforward neural networks. …

… reinforcement learning (DRL), which is an indepth combination of artificial neural network
(ANN) and reinforcement learning … to the multiple layers of neural network that replicate the …