Update README.md

README.md

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 This project implements a Deep Q-Network (DQN) to train an agent to solve the Lunar Lander environment from OpenAI Gym. The goal is to teach the agent to safely control a lunar lander to land on the moon's surface by interacting with the environment.
 
 The project includes:
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 - A fully implemented DQN algorithm.
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 - Real-time visualization of the training process using Pygame.
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 - Dynamic plotting of training progress using Matplotlib.
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 You can modify the following hyperparameters in the script to customize training:
 
 **Learning Rate:** LR (default: 5e-4)
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 **Bactch Size:** BATCH_SIZE (default: 64)
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 **Discount Factor (Gamma):** GAMMA (default: 0.99)
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 **Replay Buffer Size:** BUFFER_SIZE (default: 1e5)
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 **Target Network Update Rate:** TAU (default: 1e-3)
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 **Update Frequency:** UPDATE_EVERY (default: 4)
 
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