![]() ![]() ![]() The approach utilised here is the deep Q-learning in which a neural network is used to approximate the Q-function. In this research, the agent used a Convolutional Neural Network (CNN) to evaluate the Q-function for a variant of Q-learning. The goal of reinforcement learning is always to maximise the expected value of the total payoff or the expected return. The researcher did not provide any information about what the bird or pipes look like to the agent, and the agent must learn these representations and directly use the input and score to develop an optimal strategy. Also, training an agent to successfully play the game is especially challenging because the motive behind this task is to afford the agent with only pixel information and the score. In order to score high, the player must keep the bird alive for as long as possible while navigating through obstacles - pipes. The bird automatically falls towards the ground due to gravity, and if it hits the ground, it dies, and the game ends. ![]() Flappy Bird is a popular mobile game in which a player tries to keep the bird alive for as long as possible while the bird flaps and navigates through the pipes. In a paper, researcher Kevin Chen showed that deep reinforcement learning is very efficient at learning how to operate the game Flappy Bird, despite the high-dimensional sensory input.Īccording to the researcher, the goal of this project is to get a policy to have an agent that can successfully play the bird game. Reinforcement learning is beneficial when we need an agent to perform a specific task, but to be precise, there is no single “correct” method of accomplishing it. Games as a testbed have rich and challenging domains for testing reinforcement learning algorithms that start with a collection of games and well-known reinforcement learning implementations. Reinforcement learning has exceeded human-level performance when it comes to playing games. ![]()
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