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 …

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 …

Biological systems, including human beings, have the innate ability to perform complex
tasks in a versatile and agile manner. Researchers in sensorimotor control have aimed to …

Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a
suitably chosen reward function. However, designing such a reward function often requires …

Intrinsically motivated spontaneous exploration is a key enabler of autonomous
developmental learning in human children. It enables the discovery of skill repertoires …

Designing effective low-level robot controllers often entail platform-specific implementations
that require manual heuristic parameter tuning, significant system knowledge, or long design …

Most policy search (PS) algorithms require thousands of training episodes to find an
effective policy, which is often infeasible with a physical robot. This survey article focuses on …

The early promise of the impact of machine intelligence did not involve the partitioning of the
nascent field of Artificial Intelligence. The founders of AI envisioned the notion of embedded …

The most data-efficient algorithms for reinforcement learning (RL) in robotics are based on
uncertain dynamical models: after each episode, they first learn a dynamical model of the …