Calendar

Most of you probably know Markov Decisions Processes (MDP). They are very useful to handle situations where an agent interacts with an environnement that may involve randomness. Concretely, at each time the MDP has a current state and the agent chooses an action : This couple state-action induces a (random) reward and a (random) state transition.  If the probability distributions for rewards and transitions are known, at least theoretically, designing optimal behaviors for the agent is easy. What about the case where these distributions are unknown at the early stage of the process ? How to LEARN optimal behaviors efficiently ? A popular way to handle this issue is to use the optimism paradigm, inspired from UCB algorithms designed for stochastic bandits problems. In this talk, I will expose the main ideas of two possible approaches, UCRL algorithm and optimistic Q-learning algorithm,  that use optimism to well perform in finite-horizon

In this talk, we will explore the concept of normal form games and their decomposition into non-strategic, harmonic, and potential games. We will begin by introducing the response graph of a game, which is a visual representation of the strategies available to each player and their corresponding utilities. What dictates the strategic interaction among players is the difference between utilities, rather than the utilities themselves. We will introduce an object that captures this behavior, called deviation flow of the game, and use it to define non-strategic, harmonic, and potential games. Finally, we will discuss the properties of these components.