In this article we hypothesise that intelligence, and its associated abilities, can be
understood as subserving the maximisation of reward. Accordingly, reward is enough to …

One obstacle to applying reinforcement learning algorithms to real-world problems is the
lack of suitable reward functions. Designing such reward functions is difficult in part because …

Success in the quest for artificial intelligence has the potential to bring unprecedented
benefits to humanity, and it is therefore worthwhile to investigate how to maximize these …

We present a suite of reinforcement learning environments illustrating various safety
properties of intelligent agents. These problems include safe interruptibility, avoiding side …

Modern reinforcement learning has been conditioned by at least three dogmas. The first is
the environment spotlight, which refers to our tendency to focus on modeling environments …

Reinforcement Learning (RL) algorithms typically utilize learning and/or planning
techniques to derive effective policies. The integration of both approaches has proven to be …

Traditional models of rational action treat the agent as though it is cleanly separated from its
environment, and can act on that environment from the outside. Such agents have a known …

The field of artificial intelligence has recently experienced a number of breakthroughs thanks
to progress in deep learning and reinforcement learning. Computer algorithms now …

This book-length article combines several peer reviewed papers and new material to
analyze the issues of ethical artificial intelligence (AI). The behavior of future AI systems can …

Bounded rationality, that is, decision-making and planning under resource limitations, is
widely regarded as an important open problem in artificial intelligence, reinforcement …