Research on human and animal behavior has long emphasized its hierarchical structure—
the divisibility of ongoing behavior into discrete tasks, which are comprised of subtask …

Foundation models pretrained on diverse data at scale have demonstrated extraordinary
capabilities in a wide range of vision and language tasks. When such models are deployed …

A generally intelligent agent faces a dilemma: it requires a complex sensorimotor space to
be capable of solving a wide range of problems, but many tasks are only feasible given the …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional
reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of …

Learning goal-directed behavior in environments with sparse feedback is a major challenge
for reinforcement learning algorithms. One of the key difficulties is insufficient exploration …

The recent success of supervised learning methods on ever larger offline datasets has
spurred interest in the reinforcement learning (RL) field to investigate whether the same …

Deep reinforcement learning has achieved many impressive results in recent years.
However, tasks with sparse rewards or long horizons continue to pose significant …

Solving complex, temporally-extended tasks is a long-standing problem in reinforcement
learning (RL). We hypothesize that one critical element of solving such problems is the …

Abstract Representation learning and option discovery are two of the biggest challenges in
reinforcement learning (RL). Proto-value functions (PVFs) are a well-known approach for …