We study provable multi-agent reinforcement learning (MARL) in the general framework of
partially observable stochastic games (POSGs). To circumvent the known hardness results …

We consider both finite-horizon and infinite-horizon versions of a dynamic game with selfish
players who observe their types privately and take actions that are publicly observed …

We formulate and analyze a general class of stochastic dynamic games with asymmetric
information arising in dynamic systems. In such games, multiple strategic agents control the …

We model attacks on a cyberphysical system as a game between two players-the attacker
and the system. The players may not acquire the complete information about each other …

Partial observability of the underlying states generally presents significant challenges for
reinforcement learning (RL). In practice, certain\emph {privileged information}, eg, the …

We consider a finite horizon dynamic game with two players who observe their types
privately and take actions, which are publicly observed. Players' types evolve as …

We analyze a class of stochastic dynamic games among teams with asymmetric information,
where members of a team share their observations internally with a delay of d. Each team is …

Cyberphysical systems (CPS) are ubiquitous in our personal and professional lives, and
they promise to dramatically improve micro-communities (eg, urban farms, hospitals), macro …

The interconnectivity of cyber and physical systems and Internet of things has created
ubiquitous concerns of cyber threats for enterprise system managers. It is common that the …

Desensitization addresses safe optimal planning under parametric uncertainties by
providing sensitivity function-based risk estimates. This paper expands upon the existing …