1,062 research outputs found
AI Researchers, Video Games Are Your Friends!
If you are an artificial intelligence researcher, you should look to video
games as ideal testbeds for the work you do. If you are a video game developer,
you should look to AI for the technology that makes completely new types of
games possible. This chapter lays out the case for both of these propositions.
It asks the question "what can video games do for AI", and discusses how in
particular general video game playing is the ideal testbed for artificial
general intelligence research. It then asks the question "what can AI do for
video games", and lays out a vision for what video games might look like if we
had significantly more advanced AI at our disposal. The chapter is based on my
keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad
audience.Comment: in Studies in Computational Intelligence Studies in Computational
Intelligence, Volume 669 2017. Springe
Evolutionary Reinforcement Learning: A Survey
Reinforcement learning (RL) is a machine learning approach that trains agents
to maximize cumulative rewards through interactions with environments. The
integration of RL with deep learning has recently resulted in impressive
achievements in a wide range of challenging tasks, including board games,
arcade games, and robot control. Despite these successes, there remain several
crucial challenges, including brittle convergence properties caused by
sensitive hyperparameters, difficulties in temporal credit assignment with long
time horizons and sparse rewards, a lack of diverse exploration, especially in
continuous search space scenarios, difficulties in credit assignment in
multi-agent reinforcement learning, and conflicting objectives for rewards.
Evolutionary computation (EC), which maintains a population of learning agents,
has demonstrated promising performance in addressing these limitations. This
article presents a comprehensive survey of state-of-the-art methods for
integrating EC into RL, referred to as evolutionary reinforcement learning
(EvoRL). We categorize EvoRL methods according to key research fields in RL,
including hyperparameter optimization, policy search, exploration, reward
shaping, meta-RL, and multi-objective RL. We then discuss future research
directions in terms of efficient methods, benchmarks, and scalable platforms.
This survey serves as a resource for researchers and practitioners interested
in the field of EvoRL, highlighting the important challenges and opportunities
for future research. With the help of this survey, researchers and
practitioners can develop more efficient methods and tailored benchmarks for
EvoRL, further advancing this promising cross-disciplinary research field
From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI
This paper reviews the field of Game AI, which not only deals with creating
agents that can play a certain game, but also with areas as diverse as creating
game content automatically, game analytics, or player modelling. While Game AI
was for a long time not very well recognized by the larger scientific
community, it has established itself as a research area for developing and
testing the most advanced forms of AI algorithms and articles covering advances
in mastering video games such as StarCraft 2 and Quake III appear in the most
prestigious journals. Because of the growth of the field, a single review
cannot cover it completely. Therefore, we put a focus on important recent
developments, including that advances in Game AI are starting to be extended to
areas outside of games, such as robotics or the synthesis of chemicals. In this
article, we review the algorithms and methods that have paved the way for these
breakthroughs, report on the other important areas of Game AI research, and
also point out exciting directions for the future of Game AI
Modelling Behavioural Diversity for Learning in Open-Ended Games
Promoting behavioural diversity is critical for solving games with
non-transitive dynamics where strategic cycles exist, and there is no
consistent winner (e.g., Rock-Paper-Scissors). Yet, there is a lack of rigorous
treatment for defining diversity and constructing diversity-aware learning
dynamics. In this work, we offer a geometric interpretation of behavioural
diversity in games and introduce a novel diversity metric based on
determinantal point processes (DPP). By incorporating the diversity metric into
best-response dynamics, we develop diverse fictitious play and diverse
policy-space response oracle for solving normal-form games and open-ended
games. We prove the uniqueness of the diverse best response and the convergence
of our algorithms on two-player games. Importantly, we show that maximising the
DPP-based diversity metric guarantees to enlarge the gamescape -- convex
polytopes spanned by agents' mixtures of strategies. To validate our
diversity-aware solvers, we test on tens of games that show strong
non-transitivity. Results suggest that our methods achieve at least the same,
and in most games, lower exploitability than PSRO solvers by finding effective
and diverse strategies.Comment: corresponds to <[email protected]
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