The mastery of skills, such as balancing an inverted pendulum, implies a very accurate
control of movements to achieve the task goals. Traditional accounts of skilled action control …

What motivates agents to explore? Successfully answering this question would enable
agents to learn efficiently in formidable tasks. Random explorations such as 𝜖-greedy are …

We present a new computing model for intrinsic rewards in reinforcement learning that
addresses the limitations of existing surprise-driven explorations. The reward is the novelty …

In order to function safely and autonomously, modern robotic systems need to understand
other agents' mental states, including their beliefs and desires about the shared …

The mastery of skills such as playing tennis or balancing an inverted pendulum implies a
very accurate control of movements to achieve the task goals. Traditional accounts of skilled …

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 …