A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study online learning in unknown Markov games with sublinear regret.
Algorithm solves online binary classification and infinite games using ERM oracle.
We introduce CSE for MLSF games and devise online learning algorithms for achieving no-external Stackelberg-regret.
New algorithms achieve near-optimal cumulative loss in nonparametric online learning and games.
New framework connects online learning to statistical learning for better generalization bounds.
Decentralized algorithm reduces regret and converges to Nash equilibrium in online congestion games.
Paper analyzes convergence rates for multi-agent learning in games.
Survey of algorithms to correct past mistakes in prediction.
We describe an approximate dynamic programming (ADP) approach to compute approximations of the optimal strategies and of the minimal losses that can be guaranteed in discounted repeated games with vector-valued losses. Such games prominently arise in the analysis of regret in repeated decision-making in adversarial env…
Progress in multiagent intelligence research is fundamentally limited by the number and quality of environments available for study. In recent years, simulated games have become a dominant research platform within reinforcement learning, in part due to their accessibility and interpretability. Previous works have targe…
Algorithm learns NE in imperfect information games with imperfect feedback.
New algorithm reduces risk in online games with limited feedback.
The in-game economies of massively multi-player online games (MMOGs) are complex systems that have to be carefully designed and managed. This paper presents the results of an analysis of auction house data from the MMOG Glitch, across a 14 month time period, the entire lifetime of the game. The data comprise almost 3 m…
New bounds show simple predictors can learn complex concepts online.
New algorithm reduces online learning regret for bounded recall games.
Algorithm learns to play against unknown opponents in sequential games.
Algorithm learns from changing zero-sum games with no regret.
Online learning algorithms have impressive convergence properties when it comes to risk minimization and convex games on very large problems. However, they are inherently sequential in their design which prevents them from taking advantage of modern multi-core architectures. In this paper we prove that online learning …
We consider online learning in an adversarial, non-convex setting under the assumption that the learner has an access to an offline optimization oracle. In the general setting of prediction with expert advice, Hazan et al. (2016) established that in the optimization-oracle model, online learning requires exponentially …
Framework for multi-agent RL with human feedback in a Snake game.
Educational game on crypto investment helps students grasp macroeconomics.
Two new RL methods enable deep learning of MFG equilibria.
A simplified Bayesian approach for online sports rating.
New RL algorithms find SNE in Markov games with myopic followers.
The Interactive Minority Game (IMG) is an online version of the traditional Minority Game in which human players can enter into competition with the traditional computer-controlled agents. Through the rich (and, importantly, analytically understood) behaviour of the MG, we can explore humans' behaviour in different kin…
We study how to adapt to smoothly-varying ('easy') environments in well-known online learning problems where acquiring information is expensive. For the problem of label efficient prediction, which is a budgeted version of prediction with expert advice, we present an online algorithm whose regret depends optimally on t…
We consider the problem of strongly-convex online optimization in presence of adversarial delays; in a T-iteration online game, the feedback of the player's query at time t is arbitrarily delayed by an adversary for d_t rounds and delivered before the game ends, at iteration t+d_t-1. Specifically for \algo{online-gradi…
Algorithm finds Nash equilibria in complex games with function approximation.
Paper addresses inefficiency in converting EFGs to NFGs for learning.
Graphon game model simplifies stochastic interactions among agents.
Machine learning detects regime shifts in online game-experiments with high accuracy.
We study the relationship between the notions of differentially private learning and online learning in games. Several recent works have shown that differentially private learning implies online learning, but an open problem of Neel, Roth, and Wu \cite{NeelAaronRoth2018} asks whether this implication is {\it efficient}…
Improved FTPL algorithm reduces regret in predictable minimax games.
Algorithm learns robust equilibrium in online Markov games with interactive data.
Adaptive OMD reduces variance in learning optimal strategies for imperfect information games.
This thesis presents some geometric insights into three different types of two player prediction games -- namely general learning task, prediction with expert advice, and online convex optimization. These games differ in the nature of the opponent (stochastic, adversarial, or intermediate), the order of the players' mo…
Paper defines a new dimension to measure self-directed learning complexity.
New research shows no-regret learning is impossible in Markov games under certain assumptions.
The General Video Game AI (GVGAI) competition and its associated software framework provides a way of benchmarking AI algorithms on a large number of games written in a domain-specific description language. While the competition has seen plenty of interest, it has so far focused on online planning, providing a forward …
The rise in online social networking has brought about a revolution in social relations. However, its effects on offline interactions and its implications for collective well-being are still not clear and are under-investigated. We study the ecology of online and offline interaction in an evolutionary game framework wh…
Paper proposes a method to predict MOBA game winners with calibrated confidence.
It is now well known that decentralised optimisation can be formulated as a potential game, and game-theoretical learning algorithms can be used to find an optimum. One of the most common learning techniques in game theory is fictitious play. However fictitious play is founded on an implicit assumption that opponents' …
Interpersonal relations are fickle, with close friendships often dissolving into enmity. In this work, we explore linguistic cues that presage such transitions by studying dyadic interactions in an online strategy game where players form alliances and break those alliances through betrayal. We characterize friendships …
Multiplayer Online Battle Arena (MOBA) is currently one of the most popular genres of digital games around the world. The domain of knowledge contained in these complicated games is large. It is hard for humans and algorithms to evaluate the real-time game situation or predict the game result. In this paper, we introdu…
The paper develops a new algorithm for constructing minimax estimators using online learning techniques.
We develop provably efficient reinforcement learning algorithms for two-player zero-sum finite-horizon Markov games with simultaneous moves. To incorporate function approximation, we consider a family of Markov games where the reward function and transition kernel possess a linear structure. Both the offline and online…
In this paper, we develop a multi-agent reinforcement learning (MARL) framework to obtain online power control policies for a large energy harvesting (EH) multiple access channel, when only causal information about the EH process and wireless channel is available. In the proposed framework, we model the online power co…
Paper resolves bias in ALFT training using generalized alignment games.