3 research outputs found
Variance decompositions for extensive-form games
Quantitative measures of randomness in games are useful for game design and
have implications for gambling law. We treat the outcome of a game as a random
variable and derive a closed-form expression and estimator for the variance in
the outcome attributable to a player of the game. We analyze poker hands to
show that randomness in the cards dealt has little influence on the outcomes of
each hand. A simple example is given to demonstrate how variance decompositions
can be used to measure other interesting properties of games
Navigating the Landscape of Multiplayer Games
Multiplayer games have long been used as testbeds in artificial intelligence
research, aptly referred to as the Drosophila of artificial intelligence.
Traditionally, researchers have focused on using well-known games to build
strong agents. This progress, however, can be better informed by characterizing
games and their topological landscape. Tackling this latter question can
facilitate understanding of agents and help determine what game an agent should
target next as part of its training. Here, we show how network measures applied
to response graphs of large-scale games enable the creation of a landscape of
games, quantifying relationships between games of varying sizes and
characteristics. We illustrate our findings in domains ranging from canonical
games to complex empirical games capturing the performance of trained agents
pitted against one another. Our results culminate in a demonstration leveraging
this information to generate new and interesting games, including mixtures of
empirical games synthesized from real world games
An Overview of Multi-Agent Reinforcement Learning from Game Theoretical Perspective
Following the remarkable success of the AlphaGO series, 2019 was a booming
year that witnessed significant advances in multi-agent reinforcement learning
(MARL) techniques. MARL corresponds to the learning problem in a multi-agent
system in which multiple agents learn simultaneously. It is an
interdisciplinary domain with a long history that includes game theory, machine
learning, stochastic control, psychology, and optimisation. Although MARL has
achieved considerable empirical success in solving real-world games, there is a
lack of a self-contained overview in the literature that elaborates the game
theoretical foundations of modern MARL methods and summarises the recent
advances. In fact, the majority of existing surveys are outdated and do not
fully cover the recent developments since 2010. In this work, we provide a
monograph on MARL that covers both the fundamentals and the latest developments
in the research frontier. The goal of our monograph is to provide a
self-contained assessment of the current state-of-the-art MARL techniques from
a game theoretical perspective. We expect this work to serve as a stepping
stone for both new researchers who are about to enter this fast-growing domain
and existing domain experts who want to obtain a panoramic view and identify
new directions based on recent advances