Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …

Abstract Model-based reinforcement learning (RL) algorithms can attain excellent sample
efficiency, but often lag behind the best model-free algorithms in terms of asymptotic …

Model-free deep reinforcement learning algorithms have been shown to be capable of
learning a wide range of robotic skills, but typically require a very large number of samples …

Reinforcement learning is an appealing approach for allowing robots to learn new tasks.
Relevant literature reveals a plethora of methods, but at the same time makes clear the lack …

Reinforcement learning (RL) can automate a wide variety of robotic skills, but learning each
new skill requires considerable real-world data collection and manual representation …

Reinforcement learning (RL) methods have been shown to be capable of learning intelligent
behavior in rich domains. However, this has largely been done in simulated domains without …

Developing control policies in simulation is often more practical and safer than directly
running experiments in the real world. This applies to policies obtained from planning and …

We present Residual Policy Learning (RPL): a simple method for improving
nondifferentiable policies using model-free deep reinforcement learning. RPL thrives in …

Despite the recent successes of deep reinforcement learning, teaching complex motor skills
to a physical robot remains a hard problem. While learning directly on a real system is …