A major challenge in reinforcement learning is to determine which state-action pairs are
responsible for future rewards that are delayed. Reward redistribution serves as a solution to …

While Reinforcement Learning (RL) achieves tremendous success in sequential decision-
making problems of many domains, it still faces key challenges of data inefficiency and the …

Learning reward-agnostic representations is an emerging paradigm in reinforcement
learning. These representations can be leveraged for several purposes ranging from reward …

The recently proposed distributional approach to reinforcement learning (DiRL) is centered
on learning the distribution of the reward-to-go, often referred to as the value distribution. In …

Diffusion models have emerged as a prominent class of generative models, surpassing
previous methods regarding sample quality and training stability. Recent works have shown …

Credit assignment in reinforcement learning is the problem of measuring an action's
influence on future rewards. In particular, this requires separating skill from luck, ie …

Many practical applications of reinforcement learning require agents to learn from sparse
and delayed rewards. It challenges the ability of agents to attribute their actions to future …

Many reinforcement learning (RL) tasks have specific properties that can be leveraged to
modify existing RL algorithms to adapt to those tasks and further improve performance, and …

Reinforcement learning is an essential paradigm for solving sequential decision problems
under uncertainty. Despite many remarkable achievements in recent decades, applying …

We study the problem of learning a good set of policies, so that when combined together,
they can solve a wide variety of unseen reinforcement learning tasks with no or very little …