Implementasi Q-Learning dan Backpropagation pada Agen yang Memainkan Permainan Flappy Bird

Ardiansyah, Ardiansyah; Rainarli, Ednawati

doi:10.22146/jnteti.v6i1.287

Cited by 5 publications

(5 citation statements)

References 2 publications

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“…The Qlearning with backpropagation required an average training time of 9 minutes and 1 second, while classical Q-learning took 120 minutes. Therefore, Q-learning with backpropagation was 92% faster than classical Q-learning with similar performance [14].…”

Section: Related Workmentioning

confidence: 93%

“…In Equation 1, there is a learning rate (α) that usually takes values between 0 and 1. The learning rate parameter (α) signifies the rate of change from the old Q-value to be replaced by the new Q-value [14], [18]. A smaller learning rate implies a slower change in Q-value, indicating that the agent is cautious in updating Q-values.…”

Section: Q-learning Implementationmentioning

confidence: 99%

“…The discount factor parameter (γ) is used to ensure that the rewards received by the agent remain bounded [14], [17]. The discount factor also influences the rewards obtained by the agent.…”

Section: Q-learning Implementationmentioning

confidence: 99%

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Application of Q-learning Method for Disaster Evacuation Route Design Case Study: Digital Center Building UNNES

Alrahma,

Anan Nugroho,

Hastawan

et al. 2024

Jurnal Ilmu Komputer dan Informasi

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The Digital Center (DC) building at UNNES is a new building on the campus that currently lacks evacuation routes. Therefore, it is necessary to create an evacuation route plan in accordance with the Minister of Health Regulation Number 48 of 2016. Creating a manual evacuation route plan can be inefficient and prone to errors, especially for large buildings with complex interiors. To address this issue, learning techniques such as reinforcement learning (RL) are being used. In this study, Q-learning will be utilized to find the shortest path to the exit doors from 11 rooms on the first floor of the DC building. The study makes use of the floor plan data of the DC building, information about the location of the exit doors, and the distance required to reach them. The results of the study demonstrate that Qlearning successfully identifies the shortest evacuation routes for the first-floor DC building. The routes generated by Q-learning are identical to the manually created shortest paths. Even when additional obstacles are introduced into the environment, Q-learning is still able to find the shortest routes. On average, the number of episodes required for convergence in both environments is less than 1000 episodes, and the average computation time needed for both environments is 0.54 seconds and 0.76 seconds, respectively.

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Section: Related Workmentioning

confidence: 93%

Section: Q-learning Implementationmentioning

confidence: 99%

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Application of Q-learning Method for Disaster Evacuation Route Design Case Study: Digital Center Building UNNES

Alrahma,

Anan Nugroho,

Hastawan

et al. 2024

Jurnal Ilmu Komputer dan Informasi

View full text Add to dashboard Cite

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“…The Deep Q Network (DQN) is a reinforcement learning algorithm developed to overcome complex problems in machine learning [20]. This algorithm is a combination of reinforcement learning with a deep artificial neural network [21].…”

Section: Deep Q Network Algorithmmentioning

confidence: 99%

Implementation of a reinforcement learning system with deep q network algorithm in the amc dash mark i game

Utomo

2024

J. Soft Comput. Explor.

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Reinforcement learning is a branch of artificial intelligence that trains algorithms using a trial-and-error system. Reinforcement learning interacts with its environment and observes the consequences of its actions in response to rewards or punishments received. Reinforcement Learning uses information from every interaction with its environment to update its knowledge. The problem identified from this research is the lack of consistency, which is not always the same for Non-Player Characters (Agents) in the process of exploring an environment (Game environment). This research uses the Software Development Life Cycle (SDLC) Waterfall model method to train Non Player Characters (Agents) in the Amc Dash Mark I Game which uses the Deep Q Network (DQN) algorithm in several stages. Training results show improvements in model performance over time. The average duration of the episode and average reward episode showed an increase of 7.75 to 24.7, while the exploration rate decreased to 0.05. This indicates that the model has experienced learning and is improving to achieve better rewards by performing fewer actions. The lower loss also shows that the model has succeeded in reducing prediction errors and improving prediction capabilities.

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“…First is Feed-forward, which is the pattern training process that will set to each unit in the input layer, then output the one generated is transmitted to the next layer, continue until the output layer. Second is backpropagation, which is the process of adjusting each weight based on the expected output, to be produced minimal error, starting from the weight connected to the output neuron, then continue to retreat until to the input layer [10].…”

Section: Identification Of Varieties Using Backpropagationmentioning

confidence: 99%

Identification of Rice Variety Using Geometric Features and Neural Network

Srimulyani¹,

Musdholifah

2019

Indonesian J. Comput. Cybern. Syst.

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Indonesia has many food varieties, one of which is rice varieties. Each rice variety has physical characteristics that can be recognized through color, texture, and shape. Based on these physical characteristics, rice can be identified using the Neural Network. Research using 12 features has not optimal results. This study proposes the addition of geometry features with Learning Vector Quantization and Backpropagation algorithms that are used separately.The trial uses data from 9 rice varieties taken from several regions in Yogyakarta. The acquisition of rice was carried out using a camera Canon D700 with a kit lens and maximum magnification, 55 mm. Data sharing is carried out for training and testing, and the training data was sharing with the quality of the rice. Preprocessing of data was carried out before feature extraction with the trial and error thresholding process of segmentation. Evaluation is done by comparing the results of the addition of 6 geometry features and before adding geometry features.The test results show that the addition of 6 geometry features gives an increase in the value of accuracy. This is evidenced by the Backpropagation algorithm resulting in increased accuracy of 100% and 5.2% the result of the LVQ algorithm.

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Implementasi Q-Learning dan Backpropagation pada Agen yang Memainkan Permainan Flappy Bird

Cited by 5 publications

References 2 publications

Application of Q-learning Method for Disaster Evacuation Route Design Case Study: Digital Center Building UNNES

Application of Q-learning Method for Disaster Evacuation Route Design Case Study: Digital Center Building UNNES

Implementation of a reinforcement learning system with deep q network algorithm in the amc dash mark i game

Identification of Rice Variety Using Geometric Features and Neural Network

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