High-dimensional observations and unknown dynamics are major challenges when
applying optimal control to many real-world decision making tasks. The Learning …

We propose WayEx, a new method for learning complex goal-conditioned robotics tasks
from a single demonstration. Our approach distinguishes itself from existing imitation …

An efficient way for a deep reinforcement learning (RL) agent to explore in sparse-rewards
settings can be to learn a set of skills that achieves a uniform distribution of terminal states …

Learning to extract the task-relevant features from high-dimensional data is an important
challenge in machine learning. The recent success of machine learning is largely …

In reinforcement learning (RL), an agent learns to solve a task by interacting with its
environment. In order to scale these RL agents on high-dimensional complex tasks, recent …

This paper investigates and develops techniques and theory for implementing state-of-the-
art model-based reinforcement learning techniques. First, neuroscientific ideas of prediction …

Reinforcement Learning (RL) has recently seen significant advances over the last decade in
simulated and controlled environments. RL has shown impressive results in difficult decision …