A Minimal Training Strategy to Play Flappy Bird Indefinitely with NEAT

Cordeiro, Matheus G.; Serafim, Paulo Bruno de Sousa; Nogueira, Yuri Lenon Barbosa; Vidal, Creto Augusto; Neto, Joaquim Bento Cavalcante

doi:10.1109/sbgames.2019.00014

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…In order to create autonomous virtual players utilizing the NEAT neuro-evolutionary method to generate an agent that can play the Flappy Bird game, this research [5] suggests a minimal training technique. The simplest neural network architecture capable of playing the game flawlessly was discovered using NEAT.…”

Section: Literature Surveymentioning

confidence: 99%

Analogical Study of Activation Concept in Neural Networks with Neat- Python Module

Kaul,

Zaman

et al. 2023

RIA

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Determining real-time machine simulation and functionalities of complex AI Engines is difficult to comprehend and is rarely discussed. We present a technique to analyze the workflow of one such engine, the NEAT engine, one of the fundamental and robust training engines in the current machine learning scenario. Computer Vision also presents a great approach towards working in real-time speedy functioning virtual simulators and visual platforms, whereas NEAT is not specialized in the same, but can perform to the best of the extent in the present day Machine Learning aspect. Technologies like Python, PyGame, and CsV were used to facilitate the research. So far, we have tested both the frameworks on real time cases, and it is safe to say that the NEAT module has presented an accurate trajectory, besides greater time complexity. Thus, we not only evaluate the accuracy but the other key factors as well. This study demonstrates that NEAT has the ability to address other difficult issues in the future and can produce excellent outcomes with a relatively small population. Robotics, artificial intelligence for video games, natural language processing, and healthcare are some of the potential future applications for NEAT.

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Section: Literature Surveymentioning

confidence: 99%

Analogical Study of Activation Concept in Neural Networks with Neat- Python Module

Kaul,

Zaman

et al. 2023

RIA

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“…The fitness function is a weighted average that relies on multiple scenarios with scenariospecific components. The neural network almost achieved perfect behaviour in the game, in which it took around 20 generations [21]. But in 2020, a new Statistical Forward Planning (SFP) method was presented.…”

Section: Related Workmentioning

confidence: 99%

Evolving and training of Neural Network to Play DAMA Board Game Using NEAT Algorithm

Qader

Jihad

Baker³

2022

IJCAI

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Neuroevolutionary algorithms, such as NeuroEvolution of Augmenting Topologies (NEAT) in Machine Learning (ML) methods, are utilized for training and playing computer games due to increased research in Artificial Intelligence (AI) field. NEAT is a genetic algorithm for the generation of evolving artificial neural networks. In this paper, a new study is presented. A Dama board game is designed, and the NEAT algorithm is implemented to develop and train the populations of neural networks for playing the game efficiently. Different inputs and outputs for the network are used, and various network sizes are tried for the game to reach or pass the human level. This paper aims to make a neural network that plays a Dama game like humans or is close by training different neural networks for many generations. The experimental results show that neural networks have been trained for several thousands of generations, and they have played more than one million games. It is concluded that using more input to handle information is better for the learning process. It is also found that a set of values for NEAT parameters is suitable for extensive neural networks like those used in this paper. Povzetek: je predstavljena nova študija. Zasnovana je tradicionalna igra, imenovana "Dama", in implementiran je algoritem NEAT za razvoj in usposabljanje populacij nevronskih omrežij, da igrajo igro, prvič, na način, ki presega človeške zmožnosti.

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“…Neuroevolution and Reinforcement learning algorithms are some of the algorithms that are used to create AI bots or artificial agents. [1], [7] and [8] have implemented a configuration of an ANN called Neuroevolution. The algorithm does not depend on the actions taken by the agents as a whole.…”

Section: Literature Surveymentioning

confidence: 99%

“…Authors in [3], [4], [6] and [7] use DNN to extract the features from the frame of the game and they form the input to the agent. However, [1], [5] and [8] make use of the game itself and place the agent to perceive its surroundings. There are several combinations of Reinforcement Learning algorithms possible, like Deep Neural Networks (DNN), Long short-term memory (LSTM), Deep Q-Network (DQN) and the like.…”

Section: Literature Surveymentioning

confidence: 99%

“…An approach to machine learning known as NEAT, or Neuroevolution of Augmenting Topologies, functions similarly to evolution. In its most basic form, [1] NEAT is a technique for creating networks that are capable of performing a certain activity, like balancing a pole or operating a robot. It's significant that NEAT networks can learn using a reward function as opposed to back-propagation.…”

Section: Introductionmentioning

confidence: 99%

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Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning

Selvan¹,

Game²

2022

Preprint

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For over a decade now, robotics and the use of artificial agents have become a common thing. Testing the performance of new path finding or search space optimisation algorithms has also become a challenge as they require simulation or an environment to test them. The creation of artificial environments with artificial agents is one of the methods employed to test such algorithms. Games have also become an environment to test them. The performance of the algorithms can be compared by using artificial agents that will behave according to the algorithm in the environment they are put in. The performance parameters can be, how quickly the agent is able to differentiate between rewarding actions and hostile actions. This can be tested by placing the agent in an environment with different types of hurdles and the goal of the agent is to reach the farthest by taking decisions on actions that will lead to avoiding all the obstacles. The environment chosen is a game called "Flappy Bird". The goal of the game is to make the bird fly through a set of pipes of random heights. The bird must go in between these pipes and must not hit the top, the bottom, or the pipes themselves. The actions that the bird can take are either to flap its wings or drop down with gravity. The algorithms that are enforced on the artificial agents are NeuroEvolution of Augmenting Topologies (NEAT) and Reinforcement Learning. The NEAT algorithm takes an 'N' initial population of artificial agents. They follow genetic algorithms by considering an objective function, crossover, mutation, and augmenting topologies. Reinforcement learning, on the other hand, remembers the state, the action taken at that state, and the reward received for the action taken using a single agent and a Deep Q-learning Network. The performance of the NEAT algorithm improves as the initial population of the artificial agents is increased.

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A Minimal Training Strategy to Play Flappy Bird Indefinitely with NEAT

Cited by 5 publications

References 12 publications

Analogical Study of Activation Concept in Neural Networks with Neat- Python Module

Analogical Study of Activation Concept in Neural Networks with Neat- Python Module

Evolving and training of Neural Network to Play DAMA Board Game Using NEAT Algorithm

Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning

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