A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
AI in Games: Techniques, Challenges and Opportunities
[article]
2021
arXiv
pre-print
Preserved Fulltext
In this paper, we survey recent successful game AIs, covering board game AIs, card game AIs, first-person shooting game AIs and real time strategy game AIs. ...
Finally, we hope this brief review can provide an introduction for beginners, inspire insights for researchers in the filed of AI in games. ...
Firstly, in the selection stage, a node is selected based on the sum of action value Q and a bonus u(p). ...
arXiv:2111.07631v1
fatcat:g4sbl6v73rg4jdijj4qfi3eusq
Combining imagination and heuristics to learn strategies that generalize
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Deep reinforcement learning can match or exceed human performance in stable contexts, but with minor changes to the environment artificial networks, unlike humans, often cannot adapt. ...
We test performance of this network using Wythoff's game, a gridworld environment with a known optimal strategy. ...
strategies in more complex environments. ...
arXiv:1809.03406v2
fatcat:g7mbudu43bgejnsv24ewvm7c2y
Self-Adapting Goals Allow Transfer of Predictive Models to New Tasks
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
A long-standing challenge in Reinforcement Learning is enabling agents to learn a model of their environment which can be transferred to solve other problems in a world with the same underlying rules. ...
Recent progress in model-based reinforcement learning has improved the ability for agents to learn and use predictive models. ...
Background
Model-based reinforcement learning The problems we target in this paper are reinforcement learning (RL) problems, meaning an agent is tasked with learning how to solve some problem without ...
arXiv:1904.02435v2
fatcat:mhkyehmllzecfmhxbmmqmvjbve
Asynchronous Advantage Actor-Critic Agent for Starcraft II
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this project we explain the complexities of this environment and discuss the results from our experiments on the environment. ...
We have compared various architectures and have proved that transfer learning can be an effective paradigm in reinforcement learning research for complex scenarios requiring skill transfer. ...
SC2LE (StarCraft II Learning Environment) [6] is a new challenge for exploring reinforcement learning algorithms and architectures, based on the StarCraft II video game. ...
arXiv:1807.08217v1
fatcat:fpiprhvfdbfk5g4vtkoysy6xlu
Multiagent Reinforcement Learning With Sparse Interactions by Negotiation and Knowledge Transfer
2017
IEEE Transactions on Cybernetics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2015 pre-print arXiv:1508.05328v1 fatcat:3rp5osbukva67pikypf7vyvsziOther Versions
Reinforcement learning has significant applications for multi-agent systems, especially in unknown dynamic environments. ...
However, most multi-agent reinforcement learning (MARL) algorithms suffer from such problems as exponential computation complexity in the joint state-action space, which makes it difficult to scale up ...
In this paper, we focus on solving learning problems in complex systems. ...
doi:10.1109/tcyb.2016.2543238
pmid:27046917
fatcat:oocm7ykxufg5tko7546gefqkr4
A Survey of Deep Reinforcement Learning in Video Games
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Deep reinforcement learning (DRL) has made great achievements since proposed. ...
We also take a review of the achievements of DRL in various video games, including classical Arcade games, first-person perspective games and multi-agent real-time strategy games, from 2D to 3D, and from ...
Reinforced Inter-Agent Learning (RIAL) and Differentiable Inter-Agent Learning (DIAL) use deep reinforcement learning to learn endto-end communication protocols in complex environments. ...
arXiv:1912.10944v2
fatcat:fsuzp2sjrfcgfkyclrsyzflax4
StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learning
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
We define an efficient state representation, which breaks down the complexity caused by the large state space in the game environment. ...
With reinforcement learning and curriculum transfer learning, our units are able to learn appropriate strategies in StarCraft micromanagement scenarios. ...
In this paper, we focus on a real-time strategy (RTS) game to explore the learning of multi-agent control. ...
arXiv:1804.00810v1
fatcat:mlskaiafdjd4rdfdtvwq7wozaa
Object-Oriented Representation and Hierarchical Reinforcement Learning in Infinite Mario
2012
2012 IEEE 24th International Conference on Tools with Artificial Intelligence
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
In this work, we analyze and improve upon reinforcement learning techniques used to build agents that can learn to play Infinite Mario, an action game. ...
We also extend the idea of hierarchical RL by designing a hierarchy in action selection using domain specific knowledge. ...
Furthermore, the agent cannot transfer its learning in between different levels of the game. An RL framework, on the other hand, is capable of this transfer.
V. ...
doi:10.1109/ictai.2012.152
dblp:conf/ictai/JoshiKSDM12
fatcat:dkj4fj6jonb57oxcsvtkwb67qy
Mutual Reinforcement Learning
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
In this paper we demonstrate the application and effectiveness of a new approach called mutual reinforcement learning (MRL), where both humans and autonomous agents act as reinforcement learners in a skill ...
An autonomous agent initially acts as an instructor who can teach a novice human participant complex skills using the MRL strategy. ...
ACKNOWLEDGMENTS This work was supported by NSF award #1527828 (NRI: Collaborative Goal and Policy Learning from Human Operators of Construction Co-Robots). ...
arXiv:1907.06725v3
fatcat:t6am5r6jzfc3pj5p664rrz7asy
Optimising market share and profit margin: SMDP-based tariff pricing under the smart grid paradigm
2014
IEEE PES Innovative Smart Grid Technologies, Europe
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
This paper puts forward a reinforcement-learning-powered tool aiding an electricity retailer to define the tariff prices it offers, in a bid to optimise its retail strategy. ...
To evaluate our trading strategy, we developed a retailer agent (termed AstonTAC) that uses the proposed SMDP framework to act in an open multi-agent simulation environment, the Power Trading Agent Competition ...
The advantage of the SMDP and reinforcement learning is that it enables the retailer agent to adapt to the strategy changes of game opponents using the environment states. ...
doi:10.1109/isgteurope.2014.7028942
fatcat:leksheha5jcdpj7t2zdabxqrai
Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Survey
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Deep reinforcement learning has shown remarkable success in the past few years. Highly complex sequential decision making problems have been solved in tasks such as game playing and robotics. ...
Model-based reinforcement learning creates an explicit model of the environment dynamics to reduce the need for environment samples. ...
ACKNOWLEDGMENTS We thank the members of the Leiden Reinforcement Learning Group, and especially Thomas Moerland and Mike Huisman, for many discussions and insights. ...
arXiv:2008.05598v2
fatcat:5xmwmemv5bfinkw57avf5ghhxq
Deep Reinforcement Learning, a textbook
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
The way in which deep reinforcement learning explores complex environments reminds us of how children learn, by playfully trying out things, getting feedback, and trying again. ...
Deep reinforcement learning has gathered much attention recently. Impressive results were achieved in activities as diverse as autonomous driving, game playing, molecular recombination, and robotics. ...
The rst AlphaGo program used supervised learning based on grandmaster games, followed by reinforcement learning on self-play games. ...
arXiv:2201.02135v2
fatcat:3icsopexerfzxa3eblpu5oal64
Transfer Learning in Real-Time Strategy Games Using Hybrid CBR/RL
2007
International Joint Conference on Artificial Intelligence
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
It uses a novel combination of Case-Based Reasoning (CBR) and Reinforcement Learning (RL) to achieve transfer while playing against the Game AI across a variety of scenarios in MadRTS TM , a commercial ...
Real Time Strategy game. ...
Acknowledgements We would like to thank Santi Ontañón for valuable feedback and the anonymous IJCAI reviewers for a variety of suggestions on this paper. ...
dblp:conf/ijcai/SharmaHSIIR07
fatcat:bbjii5zvfbg7fay5ftj52gyr64
Transfer Learning in Attack Avoidance Games
2020
Journal of Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this study, we present a new strategy to facilitate knowledge transfer when an agent is learning to solve a sequence of increasing difficulty tasks. ...
Transfer knowledge is a human characteristic that has been replicated in machine learning algorithms to improve learning performance measures. ...
Each one of these strategies finds knowledge in a different location and based on that implements a method to transfer it. ...
doi:10.3844/jcssp.2020.1465.1476
fatcat:iqea3cqpvbdubgk7mmrxybnlcu
Towards autonomous behavior learning of non-player characters in games
2016
Expert systems with applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
in one framework. ...
In view of their complementary strengths, this paper proposes a computational model unifying the two learning paradigms based on a class of self-organizing neural networks called Fusion Architecture for ...
In the game environment, each NPC is essentially an autonomous agent, which is expected to function and adapt by themselves in a complex and dynamic environment. ...
doi:10.1016/j.eswa.2016.02.043
fatcat:vfsatkxgdfcv7is3gb4kgatydm
« Previous
Showing results 1 — 15 out of 59,835 results