Deep reinforcement learning (DRL) has been applied to a variety of problems during the
past decade and has provided effective control strategies in high-dimensional and non …

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …

Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

Deep reinforcement learning (deep RL) holds the promise of automating the acquisition of
complex controllers that can map sensory inputs directly to low-level actions. In the domain …

Reinforcement learning is a powerful paradigm for learning optimal policies from
experimental data. However, to find optimal policies, most reinforcement learning algorithms …

Model-free reinforcement learning (RL) methods are succeeding in a growing number of
tasks, aided by recent advances in deep learning. However, they tend to suffer from high …

We introduce an information theoretic model predictive control (MPC) algorithm capable of
handling complex cost criteria and general nonlinear dynamics. The generality of the …

Abstract Model-free reinforcement learning has been successfully applied to a range of
challenging problems, and has recently been extended to handle large neural network …

Policy gradients methods apply to complex, poorly understood, control problems by
performing stochastic gradient descent over a parameterized class of polices. Unfortunately …

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine
learning paradigm that continuously learns by accumulating past knowledge that it then …