Learning about rewards and punishments is critical for survival. Classical studies have
demonstrated an impressive correspondence between the firing of dopamine neurons in the …

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

Many reinforcement learning (RL) problems in practice are offline, learning purely from
observational data. A key challenge is how to ensure the learned policy is safe, which …

The hypothesis that midbrain dopamine (DA) neurons broadcast a reward prediction error
(RPE) is among the great successes of computational neuroscience. However, recent …

Safety is critical to broadening the real-world use of reinforcement learning. Modeling the
safety aspects using a safety-cost signal separate from the reward and bounding the …

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

We analyse quantile temporal-difference learning (QTD), a distributional reinforcement
learning algorithm that has proven to be a key component in several successful large-scale …

When faced with sequential decision-making problems, it is often useful to be able to predict
what would happen if decisions were made using a new policy. Those predictions must …

Offline reinforcement learning (RL) shows promise of applying RL to real-world problems by
effectively utilizing previously collected data. Most existing offline RL algorithms use …

In fully cooperative multi-agent reinforcement learning (MARL) settings, the environments
are highly stochastic due to the partial observability of each agent and the continuously …