The Design Of "Stratega": A General Strategy Games Framework
- URL: http://arxiv.org/abs/2009.05643v1
- Date: Fri, 11 Sep 2020 20:02:00 GMT
- Title: The Design Of "Stratega": A General Strategy Games Framework
- Authors: Diego Perez-Liebana, Alexander Dockhorn, Jorge Hurtado Grueso, Dominik
Jeurissen
- Abstract summary: Stratega is a framework for creating turn-based and real-time strategy games.
The framework has been built with a focus on statistical forward planning (SFP) agents.
We hope that the development of this framework and its respective agents helps to better understand the complex decision-making process in strategy games.
- Score: 62.997667081978825
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Stratega, a general strategy games framework, has been designed to foster
research on computational intelligence for strategy games. In contrast to other
strategy game frameworks, Stratega allows to create a wide variety of
turn-based and real-time strategy games using a common API for agent
development. While the current version supports the development of turn-based
strategy games and agents, we will add support for real-time strategy games in
future updates. Flexibility is achieved by utilising YAML-files to configure
tiles, units, actions, and levels. Therefore, the user can design and run a
variety of games to test developed agents without specifically adjusting it to
the game being generated. The framework has been built with a focus of
statistical forward planning (SFP) agents. For this purpose, agents can access
and modify game-states and use the forward model to simulate the outcome of
their actions. While SFP agents have shown great flexibility in general
game-playing, their performance is limited in case of complex state and
action-spaces. Finally, we hope that the development of this framework and its
respective agents helps to better understand the complex decision-making
process in strategy games. Stratega can be downloaded at:
https://github.research.its.qmul.ac.uk/eecsgameai/Stratega
Related papers
- Paths to Equilibrium in Games [6.812247730094933]
We study sequences of strategies satisfying a pairwise constraint inspired by policy updating in reinforcement learning.
Our analysis reveals a counterintuitive insight that reward deteriorating strategic updates are key to driving play to equilibrium along a satisficing path.
arXiv Detail & Related papers (2024-03-26T19:58:39Z) - Game Theoretic Rating in N-player general-sum games with Equilibria [26.166859475522106]
We propose novel algorithms suitable for N-player, general-sum rating of strategies in normal-form games according to the payoff rating system.
This enables well-established solution concepts, such as equilibria, to be leveraged to efficiently rate strategies in games with complex strategic interactions.
arXiv Detail & Related papers (2022-10-05T12:33:03Z) - Portfolio Search and Optimization for General Strategy Game-Playing [58.896302717975445]
We propose a new algorithm for optimization and action-selection based on the Rolling Horizon Evolutionary Algorithm.
For the optimization of the agents' parameters and portfolio sets we study the use of the N-tuple Bandit Evolutionary Algorithm.
An analysis of the agents' performance shows that the proposed algorithm generalizes well to all game-modes and is able to outperform other portfolio methods.
arXiv Detail & Related papers (2021-04-21T09:28:28Z) - Generating Diverse and Competitive Play-Styles for Strategy Games [58.896302717975445]
We propose Portfolio Monte Carlo Tree Search with Progressive Unpruning for playing a turn-based strategy game (Tribes)
We show how it can be parameterized so a quality-diversity algorithm (MAP-Elites) is used to achieve different play-styles while keeping a competitive level of play.
Our results show that this algorithm is capable of achieving these goals even for an extensive collection of game levels beyond those used for training.
arXiv Detail & Related papers (2021-04-17T20:33:24Z) - Deep Policy Networks for NPC Behaviors that Adapt to Changing Design
Parameters in Roguelike Games [137.86426963572214]
Turn-based strategy games like Roguelikes, for example, present unique challenges to Deep Reinforcement Learning (DRL)
We propose two network architectures to better handle complex categorical state spaces and to mitigate the need for retraining forced by design decisions.
arXiv Detail & Related papers (2020-12-07T08:47:25Z) - DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games [137.86426963572214]
We introduce DeepCrawl, a fully-playable Roguelike prototype for iOS and Android in which all agents are controlled by policy networks trained using Deep Reinforcement Learning (DRL)
Our aim is to understand whether recent advances in DRL can be used to develop convincing behavioral models for non-player characters in videogames.
arXiv Detail & Related papers (2020-12-03T13:53:29Z) - Efficient exploration of zero-sum stochastic games [83.28949556413717]
We investigate the increasingly important and common game-solving setting where we do not have an explicit description of the game but only oracle access to it through gameplay.
During a limited-duration learning phase, the algorithm can control the actions of both players in order to try to learn the game and how to play it well.
Our motivation is to quickly learn strategies that have low exploitability in situations where evaluating the payoffs of a queried strategy profile is costly.
arXiv Detail & Related papers (2020-02-24T20:30:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.