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 …

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 …

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 …

Deep reinforcement learning (DRL) has led to a wide range of advances in sequential
decision-making tasks. However, the complexity of neural network policies makes it difficult …

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

The recent advancements in Deep Reinforcement Learning (DRL) have significantly
enhanced the performance of adaptive Traffic Signal Control (TSC). However, DRL policies …

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 …

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 …

We present a verification-based learning framework VEL that synthesizes safe programmatic
controllers for environments with continuous state and action spaces. The key idea is the …

Programmatically Interpretable Reinforcement Learning (PIRL) encodes policies in human-
readable computer programs. Novel algorithms were recently introduced with the goal of …