MDPs with low-rank transitions—that is, the transition matrix can be factored into the product
of two matrices, left and right—is a highly representative structure that enables tractable …

Convex Reinforcement Learning (RL) is a recently introduced framework that generalizes
the standard RL objective to any convex (or concave) function of the state distribution …

We consider the reinforcement learning (RL) problem with general utilities which consists in
maximizing a function of the state-action occupancy measure. Beyond the standard …

In this paper, we study the expressivity of scalar, Markovian reward functions in
Reinforcement Learning (RL), and identify several limitations to what they can express …

In reinforcement learning and imitation learning, an object of central importance is the state
distribution induced by the policy. It plays a crucial role in the policy gradient theorem, and …

In reinforcement learning (RL), rewards of states are typically considered additive, and
following the Markov assumption, they are $\textit {independent} $ of states visited …

Diffusion regulates numerous natural processes and the dynamics of many successful
generative models. Existing models to learn the diffusion terms from observational data rely …

In classic Reinforcement Learning (RL), the agent maximizes an additive objective of the
visited states, eg, a value function. Unfortunately, objectives of this type cannot model many …

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 …

We study the problem of learning a Nash equilibrium (NE) in Markov games which is a
cornerstone in multi-agent reinforcement learning (MARL). In particular, we focus on infinite …