To train a neural network to play the Google Chrome Dino game, we can use reinforcement learning. Reinforcement learning is a type of machine learning where an agent learns to take actions in an environment to maximize a cumulative reward signal.
To get started, we need to set up an environment for the game to run in. We can use the Python package Pygame to create a game window and capture the screen. Then we need to pre-process the game screen to extract information about the obstacles and player position.
Next, we can build a deep neural network using a framework such as TensorFlow or Keras. The input to the neural network will be the pre-processed game screen, and the output will be the action to take (jump or not jump).
We can use a technique called Q-learning to train the neural network. Q-learning is a type of reinforcement learning where the agent learns to estimate the expected future reward for each action in each state. We can update the neural network's parameters using the Q-learning update rule, which maximizes the expected future reward.
Finally, we can test the trained neural network by running the game and having the agent take actions based on its output. With sufficient training, the neural network should be able to play the game at a human-like level or better!
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