Value functions are a central component of deep reinforcement learning (RL). These
functions, parameterized by neural networks, are trained using a mean squared error …

Adaptive behavior often requires predicting future events. The theory of reinforcement
learning prescribes what kinds of predictive representations are useful and how to compute …

This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement
learning and sequential decision making, covering value-based RL, policy-gradient …

This paper contributes a new approach for distributional reinforcement learning which
elucidates a clean separation of transition structure and reward in the learning process …

In Deep Reinforcement Learning (RL), it is a challenge to learn representations that do not
exhibit catastrophic forgetting or interference in non-stationary environments. Successor …

As AI systems play a progressively larger role in human affairs, it becomes more important
that these systems are built with insights from human behavior. In particular, models that are …