Partial observability is ubiquitous in applications of Reinforcement Learning (RL), in which
agents learn to make a sequence of decisions despite lacking complete information about …

Abstract We study Reinforcement Learning for partially observable systems using function
approximation. We propose a new PO-bilinear framework, that is general enough to include …

This paper introduces a simple efficient learning algorithms for general sequential decision
making. The algorithm combines Optimism for exploration with Maximum Likelihood …

Much of reinforcement learning theory is built on top of oracles that are computationally hard
to implement. Specifically for learning near-optimal policies in Partially Observable Markov …

In this paper we study online Reinforcement Learning (RL) in partially observable dynamical
systems. We focus on the Predictive State Representations (PSRs) model, which is an …

POMDPs capture a broad class of decision making problems, but hardness results suggest
that learning is intractable even in simple settings due to the inherent partial observability …

We study sample efficient reinforcement learning (RL) under the general framework of
interactive decision making, which includes Markov decision process (MDP), partially …

We study off-policy evaluation (OPE) for partially observable MDPs (POMDPs) with general
function approximation. Existing methods such as sequential importance sampling …

This paper studies the fundamental limits of reinforcement learning (RL) in the challenging
partially observable setting. While it is well-established that learning in Partially Observable …

This paper investigates posterior sampling algorithms for competitive reinforcement learning
(RL) in the context of general function approximations. Focusing on zero-sum Markov games …