As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …

Model-free reinforcement learning (RL) can be used to learn effective policies for complex
tasks, such as Atari games, even from image observations. However, this typically requires …

We present foundations for using Model Predictive Control (MPC) as a differentiable policy
class for reinforcement learning. This provides one way of leveraging and combining the …

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 …

Abstract We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep
reinforcement learning combining model-free and model-based aspects. In contrast to most …

Deep reinforcement learning (RL) has proven a powerful technique in many sequential
decision making domains. However, Robotics poses many challenges for RL, most notably …

Model-free reinforcement learning (RL) is a powerful, general tool for learning complex
behaviors. However, its sample efficiency is often impractically large for solving challenging …

We present an information-theoretic approach to stochastic optimal control problems that
can be used to derive general sampling-based optimization schemes. This new …

We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep
reinforcement learning combining model-free and model-based aspects. In contrast to most …

Abstract Model-based reinforcement learning algorithms with probabilistic dynamical
models are amongst the most data-efficient learning methods. This is often attributed to their …