arXiv research

This work tackles learning Markov games with adversarial opponents and achieves both average reward and exploitation.

Algorithm learns to play against unknown opponents in sequential games.

Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers. However, an attacker is not usually able to directly modify another agent's observations. This might lead one to wonder: is it possible to attack an R…

We introduce Threatened Markov Decision Processes (TMDPs) as an extension of the classical Markov Decision Process framework for Reinforcement Learning (RL). TMDPs allow suporting a decision maker against potential opponents in a RL context. We also propose a level-k thinking scheme resulting in a novel learning approa…

DORIS algorithm achieves no-regret learning in Markov games with adversarial opponents.

Bayesian methods improve adversarial machine learning robustness.

Study optimal policy regret in partially observable Markov games with adaptive opponents.

Study on learning strategies in adaptive Markov games with policy regret as metric.

New algorithms improve on bandit feedback in matrix games with unknown payoff matrices.

Novel approach models opponent learning dynamics in multi-agent reinforcement learning.

This paper proposes using variational autoencoders to model opponents in multi-agent systems.

The Generative Adversarial Networks (GANs) have demonstrated impressive performance for data synthesis, and are now used in a wide range of computer vision tasks. In spite of this success, they gained a reputation for being difficult to train, what results in a time-consuming and human-involved development process to u…

LAFF algorithm balances adaptability and non-exploitability in repeated games.

New algorithm improves self-play reinforcement learning for competitive games.

Study online learning in unknown Markov games with sublinear regret.

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…

New adversarial training method improves robustness of power system controllers.

This paper investigates generalisation in multi-agent games, where the generality of the agent can be evaluated by playing against opponents it hasn't seen during training. We propose two new games with concealed information and complex, non-transitive reward structure (think rock/paper/scissors). It turns out that mos…

Algorithm improves RL model selection for repeated games with utility maximization.

New algorithm reduces online learning regret in uninformed Markov games.

The paper develops algorithms for competitive RL in partially observable MGs.

A new method uses counterfactual learning to improve recommendation system evaluation.

Colour vision has long fascinated scientists, who have sought to understand both the physiology of the mechanics of colour vision and the psychophysics of colour perception. We consider representations of colour in anatomically constrained convolutional deep neural networks. Following ideas from neuroscience, we classi…

We consider the problem of high-level strategy selection in the adversarial setting of real-time strategy games from a reinforcement learning perspective, where taking an action corresponds to switching to the respective strategy. Here, a good strategy successfully counters the opponent's current and possible future st…

The dynamics of financial markets are driven by the interactions between participants, as well as the trading mechanisms and regulatory frameworks that govern these interactions. Decision-makers would rather not ignore the impact of other participants on these dynamics and should employ tools and models that take this …

Humans are capable of attributing latent mental contents such as beliefs or intentions to others. The social skill is critical in daily life for reasoning about the potential consequences of others' behaviors so as to plan ahead. It is known that humans use such reasoning ability recursively by considering what others …

This work improves autonomous racing by creating diverse opponents and adapting risk.

A new method simulates large, diverse populations of learning agents evolving in games.

Study best-response learning dynamics in zero-sum polymatrix games under full and minimal information settings.

The study classifies and imitates trading agents in financial markets.

AI beats 95% of humans in Rock-Paper-Scissors.

Improved learning algorithm for first-price auctions reduces regret significantly.

Fictitious play with reinforcement learning is a general and effective framework for zero-sum games. However, using the current deep neural network models, the implementation of fictitious play faces crucial challenges. Neural network model training employs gradient descent approaches to update all connection weights, …

Algorithm converges to Nash equilibria in competitive games.

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' …

Algorithm minimizes regret and converges to equilibria in Markov games.

Optimistic Mirror Descent framework improves bidding strategies in non-stationary first-price auctions.

Deep RL drone trained to compete against classical path planning in drone racing.

Automatically finds effective security strategies through reinforcement learning and self-play.

This paper simplifies complex game dynamics by using a recursive representation.

In this paper, we employ machine learning techniques to analyze seventeen seasons (1999-2000 to 2015-2016) of NBA regular season data from every team to determine the common characteristics among NBA playoff teams. Each team was characterized by 26 predictor variables and one binary response variable taking on a value …

This paper tackles sample-efficient reinforcement learning for partially observable Markov games.

We present the quantum model of Bertrand duopoly and study the entanglement behavior on the profit functions of the firms. Using the concept of optimal response of each firm to the price of the opponent, we found only one Nash equilibirum point for maximally entangled initial state. The very presence of quantum entangl…

New algorithms prove self-play can be effective in competitive RL.

Calibrated strategies can be obtained by performing strategies that have no internal regret in some auxiliary game. Such strategies can be constructed explicitly with the use of Blackwell's approachability theorem, in an other auxiliary game. We establish the converse: a strategy that approaches a convex $B$-set can be…

In this paper, we perform a minimalistic quantization of the classical game of tic-tac-toe, by allowing superpositions of classical moves. In order for the quantum game to reduce properly to the classical game, we require legal quantum moves to be orthogonal to all previous moves. We also admit interference effects, by…

First-best climate policy is a uniform carbon tax which gradually rises over time. Civil servants have complicated climate policy to expand bureaucracies, politicians to create rents. Environmentalists have exaggerated climate change to gain influence, other activists have joined the climate bandwagon. Opponents to cli…

We consider Blackwell approachability, a very powerful and geometric tool in game theory, used for example to design strategies of the uninformed player in repeated games with incomplete information. We extend this theory to "generalized quitting games" , a class of repeated stochastic games in which each player may ha…