The objective of lifelong reinforcement learning (RL) is to optimize agents which can
continuously adapt and interact in changing environments. However, current RL approaches …

Humans and animals explore their environment and acquire useful skills even in the
absence of clear goals, exhibiting intrinsic motivation. The study of intrinsic motivation in …

Biological systems often choose actions without an explicit reward signal, a phenomenon
known as intrinsic motivation. The computational principles underlying this behavior remain …

The reward function is an essential component in robot learning. Reward directly affects the
sample and computational complexity of learning, and the quality of a solution. The design …

Exploration in the absence of a concrete task is a key characteristic of autonomous agents
and vital for the emergence of intelligent behaviour. Various intrinsic motivation frameworks …

Humans and animals explore their environment and acquire useful skills even in the
absence of clear goals, exhibiting intrinsic motivation. The study of intrinsic motivation in …

Learning to control robots without human supervision and prolonged engineering effort has
been a long-term dream in the intersection of machine learning and robotics. If successful, it …

Reinforcement learning has been a powerful tool for building continuously improving
systems in domains like video games and animated character control, but has proven …

Building general purpose RL algorithms that can efficiently solve a wide variety of problems
will require encoding the right structure and representations into our models. A key …