Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning

Jerin Paul Selvan; Pravin S. Game

Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning

Authors: Jerin Paul Selvan, Pravin S. Game

arXiv: 2207.14140v1 - DOI (cs.LG)

5 pages, 7 figures, 3 tables

License: CC BY-SA 4.0

Results of the summarizing process for the arXiv paper: 2207.14140v1

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In recent years, the use of robotics and artificial agents has become increasingly common. To address the challenge of testing the performance of new path finding or search space optimization algorithms, artificial environments with artificial agents have been created. Games such as "Flappy Bird" provide an effective way to test these algorithms by allowing for more efficient determination of algorithm performance compared to simulations. Two algorithms that have been enforced on artificial agents in "Flappy Bird" are NeuroEvolution of Augmenting Topologies (NEAT) and Reinforcement Learning. The NEAT algorithm follows genetic algorithms by considering an objective function, crossover, mutation, and augmenting topologies. Reinforcement learning remembers the state, action taken at that state, and reward received for the action taken using a single agent and a Deep Q-learning Network. Through various iterations by changing the initial population size in "Flappy Bird," researchers found that the average score gained by the agent increased. In conclusion, games like "Flappy Bird" provide an effective way to test path finding or search space optimization algorithms through artificial environments with artificial agents. The use of NEAT and Reinforcement Learning algorithms has shown promising results in improving agent performance; further research could explore the potential of other algorithms such as those involving multi-dimensional image problems or hidden layers.

Robotics and artificial agents are increasingly common in recent years
Artificial environments with artificial agents have been created to test new path finding or search space optimization algorithms
Games like "Flappy Bird" provide an effective way to test these algorithms compared to simulations
Two algorithms enforced on artificial agents in "Flappy Bird" are NEAT and Reinforcement Learning
NEAT considers an objective function, crossover, mutation, and augmenting topologies while Reinforcement learning remembers the state, action taken at that state, and reward received for the action taken using a single agent and a Deep Q-learning Network
Researchers found that changing the initial population size in "Flappy Bird" increased the average score gained by the agent
The use of NEAT and Reinforcement Learning algorithms has shown promising results in improving agent performance
Further research could explore other potential algorithms involving multi-dimensional image problems or hidden layers.

Testing Path Finding and Search Space Optimization Algorithms with Artificial Agents in "Flappy Bird"

NEAT and Reinforcement Learning Algorithms

Two algorithms that have been enforced on artificial agents in "Flappy Bird" are NeuroEvolution of Augmenting Topologies (NEAT) and Reinforcement Learning. The NEAT algorithm follows genetic algorithms by considering an objective function, crossover, mutation, and augmenting topologies. Reinforcement learning remembers the state, action taken at that state, and reward received for the action taken using a single agent and a Deep Q-learning Network.

Improved Agent Performance Through Iterations

Through various iterations by changing the initial population size in "Flappy Bird," researchers found that the average score gained by the agent increased. In conclusion, games like "Flappy Bird" provide an effective way to test path finding or search space optimization algorithms through artificial environments with artificial agents. The use of NEAT and Reinforcement Learning algorithms has shown promising results in improving agent performance; further research could explore the potential of other algorithms such as those involving multi-dimensional image problems or hidden layers.

Created on 29 Apr. 2023

AI-generated keywords: Robotics Artificial Agents Path Finding Reinforcement Learning NEAT

AI-generated Key Points

Robotics and artificial agents are increasingly common in recent years
Artificial environments with artificial agents have been created to test new path finding or search space optimization algorithms
Games like "Flappy Bird" provide an effective way to test these algorithms compared to simulations
Two algorithms enforced on artificial agents in "Flappy Bird" are NEAT and Reinforcement Learning
NEAT considers an objective function, crossover, mutation, and augmenting topologies while Reinforcement learning remembers the state, action taken at that state, and reward received for the action taken using a single agent and a Deep Q-learning Network
Researchers found that changing the initial population size in "Flappy Bird" increased the average score gained by the agent
The use of NEAT and Reinforcement Learning algorithms has shown promising results in improving agent performance
Further research could explore other potential algorithms involving multi-dimensional image problems or hidden layers.

Also access our AI generated: Comprehensive summary, Lay summary, Blog-like article; or ask questions about this paper to our AI assistant.

Abstract: 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 optimization 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.

Submitted to arXiv on 28 Jul. 2022

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