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Found 17 papers, 1 papers with code
Learning Adversarial MDPs with Stochastic Hard Constraints
To the best of our knowledge, our work is the first to study CMDPs involving both adversarial losses and hard constraints.
Markov Persuasion Processes: Learning to Persuade from Scratch
Recently, Markov persuasion processes (MPPs) have been introduced to capture sequential scenarios where a sender faces a stream of myopic receivers in a Markovian environment.
Learning Optimal Contracts: How to Exploit Small Action Spaces
We design an algorithm that learns an approximately-optimal contract with high probability in a number of rounds polynomial in the size of the outcome space, when the number of actions is constant.
A Best-of-Both-Worlds Algorithm for Constrained MDPs with Long-Term Constraints
We study online learning in episodic constrained Markov decision processes (CMDPs), where the goal of the learner is to collect as much reward as possible over the episodes, while guaranteeing that some long-term constraints are satisfied during the learning process.
A Unifying Framework for Online Optimization with Long-Term Constraints
We present the first best-of-both-world type algorithm for this general class of problems, with no-regret guarantees both in the case in which rewards and constraints are selected according to an unknown stochastic model, and in the case in which they are selected at each round by an adversary.
Sequential Information Design: Learning to Persuade in the Dark
At each round, the sender observes the realizations of random events in the SDM problem.
Exploiting Opponents Under Utility Constraints in Sequential Games
Recently, game-playing agents based on AI techniques have demonstrated super-human performance in several sequential games, such as chess, Go, and poker.
Multi-Receiver Online Bayesian Persuasion
Then, we focus on the case of submodular sender's utility functions and we show that, in this case, it is possible to design a polynomial-time no-$(1 - \frac{1}{e})$-regret algorithm.
Simple Uncoupled No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium
The existence of simple uncoupled no-regret learning dynamics that converge to correlated equilibria in normal-form games is a celebrated result in the theory of multi-agent systems.
Trembling-Hand Perfection and Correlation in Sequential Games
After providing an axiomatic definition of EFPCE, we show that one always exists since any perfect (Nash) equilibrium constitutes an EFPCE, and that it is a refinement of EFCE, as any EFPCE is also an EFCE.
Computer Science and Game Theory
Online Posted Pricing with Unknown Time-Discounted Valuations
We evaluate our mechanisms in terms of competitive ratio, measuring the worst-case ratio between their revenue and that of an optimal mechanism that knows the distribution of valuations.
Computer Science and Game Theory
Online Bayesian Persuasion
We are interested in no-regret algorithms prescribing a signaling scheme at each round of the repeated interaction with performances close to that of the best-in-hindsight signaling scheme.
No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium
When each player has low trigger regret, the empirical frequency of play is close to an EFCE.
Signaling in Bayesian Network Congestion Games: the Subtle Power of Symmetry
A natural question is the following: is it possible for an informed sender to reduce the overall social cost via the strategic provision of information to players who update their beliefs rationally?
Learning Probably Approximately Correct Maximin Strategies in Simulation-Based Games with Infinite Strategy Spaces
As a result, solving these games begets the challenge of designing learning algorithms that can find (approximate) equilibria with high confidence, using as few simulator queries as possible.
Learning to Correlate in Multi-Player General-Sum Sequential Games
In the context of multi-player, general-sum games, there is an increasing interest in solution concepts modeling some form of communication among players, since they can lead to socially better outcomes with respect to Nash equilibria, and may be reached through learning dynamics in a decentralized fashion.
Computer Science and Game Theory
Computing the Strategy to Commit to in Polymatrix Games (Extended Version)
We study the computational complexity of finding or approximating an optimistic or pessimistic leader-follower equilibrium in specific classes of succinct games---polymatrix like---which are equivalent to 2-player Bayesian games with uncertainty over the follower, with interdependent or independent types.
