Articles

Diagram of MURAL, our method for learning uncertainty-aware rewards for RL. After the user provides a few examples of desired outcomes, MURAL automatically infers a reward function that takes into account these examples and the agent’s uncertainty for each state. Although reinforcement learning has shown success in domains such as robotics, chip placement and playing …

Making RL tractable by learning more informative reward functions: example-based control, meta-learning, and normalized maximum likelihood Read More »

How do humans become so skillful? Well, initially we are not, but from infancy, we discover and practice increasingly complex skills through self-supervised play. But this play is not random – the child development literature suggests that infants use their prior experience to conduct directed exploration of affordances like movability, suckability, graspability, and digestibility through …

What can I do here? Learning new skills by imagining visual affordances Read More »