A major goal of artificial intelligence (AI) is to create an agent capable of acquiring a general
understanding of the world. Such an agent would require the ability to continually …

In recent years, reinforcement learning (RL) systems have shown impressive performance
and remarkable achievements. Many achievements can be attributed to combining RL with …

Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake domains …

Abstract Large Language Models (LLMs) have demonstrated proficiency in utilizing various
tools by coding, yet they face limitations in handling intricate logic and precise control. In …

In this work we consider Code World Models, world models generated by a Large Language
Model (LLM) in the form of Python code for model-based Reinforcement Learning (RL) …

Deep reinforcement learning agents are prone to goal misalignments. The black-box nature
of their policies hinders the detection and correction of such misalignments, and the trust …

Collectiveness is an important property of many systems—both natural and artificial. By
exploiting a large number of individuals, it is often possible to produce effects that go far …

Goal-conditioned reinforcement learning (RL) is a powerful approach for learning general-
purpose skills by reaching diverse goals. However, it has limitations when it comes to task …

The synthesis of programmatic strategies requires one to search in large non-differentiable
spaces of computer programs. Current search algorithms use self-play approaches to guide …

Programmatic reinforcement learning (PRL) has been explored for representing policies
through programs as a means to achieve interpretability and generalization. Despite …