Past seminars
Prof. Tamer Başar
University of Illinois at Urbana-Champaign
Video | Slides
Multi-Agent Dynamical Systems with Asymmetric Information and with Elements of Learning
Decision making in dynamic uncertain environments with multiple agents arises in many disciplines and application domains, including control, communications, distributed optimization, social networks, and economics. Here a natural framework, and a comprehensive one, for modeling, optimization, and analysis is the one provided by stochastic dynamic games (SDGs), which accommodates different solution concepts depending on how the interactions among the agents are modeled, particularly whether they are in a cooperative mode (with the same objective functions, as in teams) or in a noncooperative mode (with different objective functions) or a mix of the two, such as teams of agents interacting noncooperatively across different teams (and of course cooperatively within each team). What also affects (strategic) interactions among the agents is the asymmetric nature of the information different agents acquire (and do not share or only partially share (selectively) with others, even within teams). What makes such problems even more challenging in a dynamic environment with networked agents is the dependence of the information available to one agent at some point in time on the policies or decisions of other agents who have already acted at earlier instants of time. Such decision problems, initially studied in a team framework, are known as those with nonclassical information where optimal policies of team agents must be designed to balance a tradeoff between contribution to optimality of the team objective function and signaling through their actions useful information to other agents in their neighborhood who would be acting after them. Existence of such a tradeoff between signaling and optimization creates even more challenging issues in SDGs with misaligned objectives among at least a subset of agents, which however can be addressed effectively for a specially structured subclass of such games, namely mean-field games.
This talk will provide an overview of the landscape above, first for a general class of stochastic dynamic teams and games, and then for a subclass where the objective functions are quadratic, and the interaction relationships are linear. The talk will also cover reinforcement learning embedded into policy development when agents do not have precise information on the underlying models.
Prof. Maryam Kamgarpour
EPFL
Learning equilibria in games with bandit feedback
A central challenge in large-scale engineering systems, such as energy and transportation networks, is enabling autonomous decision-making among interacting agents. Game theory provides a natural framework to model and analyze such problems. In practice, however, agents often have only partial information about the costs and actions of others. This makes decentralized learning a key tool for developing effective strategies. In this talk, I will discuss recent advances in decentralized learning for static and Markov games under bandit feedback. I will outline algorithms with convergence guarantees and highlight directions for future research.
Giona Fieni
ETH Zürich
Video
Game Theory in Formula One
Prof. David Fridovich-Keil
University of Texas at Austin
Video | Slides
Variations on a Theme: Information Structure, Equilibria, and Dynamic Games
This talk reviews a fundamental building block of dynamic game theory—the linear-quadratic game—and discuss how Nash equilibrium solutions differ as a consequence of the information players have access to at different times. In this context, we examine several recent results, aligned to the following questions:
- How can we find feedback strategies which closely approximate Nash solutions, but minimize inter-agent communication/sensing?
- If agents’ access to information changes during an interaction, are there scenarios in which we can still find equilibria efficiently?
- In two-player, zero-sum games, there are classical results about the equivalence of solutions under different information structures for linear-quadratic games. In what sense do these extend beyond the linear-quadratic setting?