853 research outputs found
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
Increasing generality in machine learning through procedural content generation
Procedural Content Generation (PCG) refers to the practice, in videogames and
other games, of generating content such as levels, quests, or characters
algorithmically. Motivated by the need to make games replayable, as well as to
reduce authoring burden, limit storage space requirements, and enable
particular aesthetics, a large number of PCG methods have been devised by game
developers. Additionally, researchers have explored adapting methods from
machine learning, optimization, and constraint solving to PCG problems. Games
have been widely used in AI research since the inception of the field, and in
recent years have been used to develop and benchmark new machine learning
algorithms. Through this practice, it has become more apparent that these
algorithms are susceptible to overfitting. Often, an algorithm will not learn a
general policy, but instead a policy that will only work for a particular
version of a particular task with particular initial parameters. In response,
researchers have begun exploring randomization of problem parameters to
counteract such overfitting and to allow trained policies to more easily
transfer from one environment to another, such as from a simulated robot to a
robot in the real world. Here we review the large amount of existing work on
PCG, which we believe has an important role to play in increasing the
generality of machine learning methods. The main goal here is to present RL/AI
with new tools from the PCG toolbox, and its secondary goal is to explain to
game developers and researchers a way in which their work is relevant to AI
research
Applied Machine Learning for Games: A Graduate School Course
The game industry is moving into an era where old-style game engines are
being replaced by re-engineered systems with embedded machine learning
technologies for the operation, analysis and understanding of game play. In
this paper, we describe our machine learning course designed for graduate
students interested in applying recent advances of deep learning and
reinforcement learning towards gaming. This course serves as a bridge to foster
interdisciplinary collaboration among graduate schools and does not require
prior experience designing or building games. Graduate students enrolled in
this course apply different fields of machine learning techniques such as
computer vision, natural language processing, computer graphics, human computer
interaction, robotics and data analysis to solve open challenges in gaming.
Student projects cover use-cases such as training AI-bots in gaming benchmark
environments and competitions, understanding human decision patterns in gaming,
and creating intelligent non-playable characters or environments to foster
engaging gameplay. Projects demos can help students open doors for an industry
career, aim for publications, or lay the foundations of a future product. Our
students gained hands-on experience in applying state of the art machine
learning techniques to solve real-life problems in gaming.Comment: The Eleventh Symposium on Educational Advances in Artificial
Intelligence (EAAI-21
Language Model-In-The-Loop: Data Optimal Approach to Learn-To-Recommend Actions in Text Games
Large Language Models (LLMs) have demonstrated superior performance in
language understanding benchmarks. CALM, a popular approach, leverages
linguistic priors of LLMs -- GPT-2 -- for action candidate recommendations to
improve the performance in text games in Jericho without environment-provided
actions. However, CALM adapts GPT-2 with annotated human gameplays and keeps
the LLM fixed during the learning of the text based games. In this work, we
explore and evaluate updating LLM used for candidate recommendation during the
learning of the text based game as well to mitigate the reliance on the human
annotated gameplays, which are costly to acquire. We observe that by updating
the LLM during learning using carefully selected in-game transitions, we can
reduce the dependency on using human annotated game plays for fine-tuning the
LLMs. We conducted further analysis to study the transferability of the updated
LLMs and observed that transferring in-game trained models to other games did
not result in a consistent transfer
An Appraisal-Based Chain-Of-Emotion Architecture for Affective Language Model Game Agents
The development of believable, natural, and interactive digital artificial
agents is a field of growing interest. Theoretical uncertainties and technical
barriers present considerable challenges to the field, particularly with
regards to developing agents that effectively simulate human emotions. Large
language models (LLMs) might address these issues by tapping common patterns in
situational appraisal. In three empirical experiments, this study tests the
capabilities of LLMs to solve emotional intelligence tasks and to simulate
emotions. It presents and evaluates a new chain-of-emotion architecture for
emotion simulation within video games, based on psychological appraisal
research. Results show that it outperforms standard LLM architectures on a
range of user experience and content analysis metrics. This study therefore
provides early evidence of how to construct and test affective agents based on
cognitive processes represented in language models
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning
Large language models excel at a variety of language tasks when prompted with
examples or instructions. Yet controlling these models through prompting alone
is limited. Tailoring language models through fine-tuning (e.g., via
reinforcement learning) can be effective, but it is expensive and requires
model access.
We propose Inference-time Policy Adapters (IPA), which efficiently tailors a
language model such as GPT-3 without fine-tuning it. IPA guides a large base
model during decoding time through a lightweight policy adaptor trained to
optimize an arbitrary user objective with reinforcement learning.
On five challenging text generation tasks, such as toxicity reduction and
open-domain generation, IPA consistently brings significant improvements over
off-the-shelf language models. It outperforms competitive baseline methods,
sometimes even including expensive fine-tuning. In particular, tailoring GPT-2
with IPA can outperform GPT-3, while tailoring GPT- 3 with IPA brings a major
performance boost over GPT-3 (and sometimes even over GPT-4). Our promising
results highlight the potential of IPA as a lightweight alternative to
tailoring extreme-scale language models
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