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 …

A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …

Reinforcement learning algorithms for real-world robotic applications must be able to handle
complex, unknown dynamical systems while maintaining data-efficient learning. These …

Many reinforcement learning (RL) tasks, especially in robotics, consist of multiple sub-tasks
that are strongly structured. Such task structures can be exploited by incorporating …

Policy gradient algorithms have shown considerable recent success in solving high-
dimensional sequential decision making tasks, particularly in robotics. However, these …

Tasks that require many sequential decisions or complex solutions are hard to solve using
conventional reinforcement learning algorithms. Based on the semi Markov decision …

Most manipulation tasks can be decomposed into a sequence of phases, where the robot's
actions have different effects in each phase. The robot can perform actions to transition …

While reward functions are an essential component of many robot learning methods,
defining such functions remains a hard problem in many practical applications. For tasks …

Learning to locomote to arbitrary goals on hardware remains a challenging problem for
reinforcement learning. In this paper, we present a hierarchical framework that improves …

This paper investigates algorithms to automatically adapt the learning rate of neural
networks (NNs). Starting with stochastic gradient descent, a large variety of learning …