9 accepted papers
Representation learning plays a crucial role in reinforcement learning, especially in complex environments with high-dimensional and unstructured states. Effective representations can enhance the efficiency of learning algorithms by improving sample efficiency and generalization across tasks. This p…
Causal discovery aims to uncover cause-and-effect relationships encoded in causal graphs by leveraging observational, interventional data, or their combination. The majority of existing causal discovery methods are developed assuming infinite interventional data. We focus on interventional data effi…
Causal knowledge about the relationships among decision variables and a reward variable in a bandit setting can accelerate the learning of an optimal decision. Current works often assume the causal graph is known, which may not always be available a priori. Motivated by this challenge, we focus on t…
Multi-agent reinforcement learning has a wide range of applications in cooperative settings, but ensuring data privacy among agents is a significant challenge. To address this challenge, we propose Privacy-Preserving Decentralized Actor-Critic (PPDAC), an algorithm that motivates agents to cooperate…
Structural causal bandit provides a framework for online decision-making problems when causal information is available. It models the stochastic environment with a structural causal model (SCM) that governs the causal relations between random variables. In each round, an agent applies an interventio…
In a variety of problems, a decision-maker is unaware of the loss function associated with a task, yet it has to minimize this unknown loss in order to accomplish the task. Furthermore, the decision-maker’s task may evolve, resulting in a varying loss function. In this setting, we explore sequential…
We consider an agent that is assigned with a temporal logic task in an environment whose semantic representation is only partially known. We represent the semantics of the environment with a set of state properties, called \emph{atomic propositions} over which, the agent holds a probabilistic belief…
We study the problem of selecting most informative subset of a large observation set to enable accurate estimation of unknown parameters. This problem arises in a variety of settings in machine learning and signal processing including feature selection, phase retrieval, and target localization. Sinc…
Automated techniques such as model checking have been used to verify models of robotic mission plans based on Markov decision processes (MDPs) and generate counterexamples that may help diagnose requirement violations. However, such artifacts may be too complex for humans to understand, because exis…