462 research outputs found
ViZDoom: DRQN with Prioritized Experience Replay, Double-Q Learning, & Snapshot Ensembling
ViZDoom is a robust, first-person shooter reinforcement learning environment,
characterized by a significant degree of latent state information. In this
paper, double-Q learning and prioritized experience replay methods are tested
under a certain ViZDoom combat scenario using a competitive deep recurrent
Q-network (DRQN) architecture. In addition, an ensembling technique known as
snapshot ensembling is employed using a specific annealed learning rate to
observe differences in ensembling efficacy under these two methods. Annealed
learning rates are important in general to the training of deep neural network
models, as they shake up the status-quo and counter a model's tending towards
local optima. While both variants show performance exceeding those of built-in
AI agents of the game, the known stabilizing effects of double-Q learning are
illustrated, and priority experience replay is again validated in its
usefulness by showing immediate results early on in agent development, with the
caveat that value overestimation is accelerated in this case. In addition, some
unique behaviors are observed to develop for priority experience replay (PER)
and double-Q (DDQ) variants, and snapshot ensembling of both PER and DDQ proves
a valuable method for improving performance of the ViZDoom Marine.Comment: 9 pages, 5 figure
Deep learning for video game playing
In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlight important open challenges in the context of applying these
machine learning methods to video games, such as general game playing, dealing
with extremely large decision spaces and sparse rewards
A short survey on modern virtual environments that utilize AI and synthetic data
Within a rather abstract computational framework Artificial Intelligence (AI) may be defined as intelligence exhibited by machines. In computer science, though, the field of AI research defines itself as the study of “intelligent agents.” In this context, interaction with popular virtual environments, as for instance in virtual game playing, has gained a lot of focus recently in the sense that it provides innovative aspects of AI perception that did not occur to researchers until now. Such aspects are typically formed by the computational intelligent behavior captured through interaction with the virtual environment, as well as the study of graphic models and biologically inspired learning techniques, like, for instance, evolutionary computation, neural networks, and reinforcement learning. In this short survey paper, we attempt to provide an overview of the most recent research works on such novel, yet quite interesting, research domains. We feel that this topic forms an attractive candidate for fellow researchers that came into sight over the last years. Thus, we initiate our study by presenting a brief overview of our motivation and continue with some basic information on recent virtual graphic models utilization and the state-of-the-art on virtual environments, which constitutes two clearly identifiable components of the herein attempted summarization. We then continue, by briefly reviewing the interesting video games territory, and by discerning and discriminating its useful types, thus envisioning possible further utilization scenarios for the collected information. A short discussion on the identified trends and a couple of future research directions conclude the paper
Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Games
Going from research to production, especially for large and complex software
systems, is fundamentally a hard problem. In large-scale game production, one
of the main reasons is that the development environment can be very different
from the final product. In this technical paper we describe an effort to add an
experimental reinforcement learning system to an existing automated game
testing solution based on scripted bots in order to increase its capacity. We
report on how this reinforcement learning system was integrated with the aim to
increase test coverage similar to [1] in a set of AAA games including
Battlefield 2042 and Dead Space (2023). The aim of this technical paper is to
show a use-case of leveraging reinforcement learning in game production and
cover some of the largest time sinks anyone who wants to make the same journey
for their game may encounter. Furthermore, to help the game industry to adopt
this technology faster, we propose a few research directions that we believe
will be valuable and necessary for making machine learning, and especially
reinforcement learning, an effective tool in game production.Comment: 8 pages, 5 figure
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