Reinforcement Learning is an area of Machine Learning focused on how agents can be
trained to make sequential decisions, and achieve a particular goal within an arbitrary …

Since its introduction, the reward prediction error theory of dopamine has explained a wealth
of empirical phenomena, providing a unifying framework for understanding the …

Reinforcement learning promises to solve complex sequential-decision problems
autonomously by specifying a high-level reward function only. However, reinforcement …

Animals make predictions to guide their behavior and update those predictions through
experience. Transient increases in dopamine (DA) are thought to be critical signals for …

Reward is the driving force for reinforcement-learning agents. This paper is dedicated to
understanding the expressivity of reward as a way to capture tasks that we would want an …

Many problems in RL, such as meta-RL, robust RL, generalization in RL, and temporal credit
assignment, can be cast as POMDPs. In theory, simply augmenting model-free RL with …

The reward hypothesis posits that," all of what we mean by goals and purposes can be well
thought of as maximization of the expected value of the cumulative sum of a received scalar …

While auxiliary tasks play a key role in shaping the representations learnt by reinforcement
learning agents, much is still unknown about the mechanisms through which this is …

The first comprehensive guide to distributional reinforcement learning, providing a new
mathematical formalism for thinking about decisions from a probabilistic perspective …

Reinforcement learning algorithms are highly sensitive to the choice of hyperparameters,
typically requiring significant manual effort to identify hyperparameters that perform well on a …