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This project provides a comprehensive understanding of reinforcement learning, focusing on Deep Q-Learning (DQN). It involves exploring the OpenAI Gym library, implementing DQN from DeepMind's seminal paper, and enhancing the DQN algorithm for improved performance and stability.

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Deep Q-Learning: Exploring OpenAI Gym Environments and Enhancing DQN for Optimal Performance

This project is aimed at providing a comprehensive understanding of reinforcement learning, specifically focusing on Deep Q-Learning (DQN). The project will involve exploring the OpenAI Gym library, implementing the DQN algorithm as described in DeepMind's seminal paper, and subsequently improving the DQN algorithm for enhanced performance and stability.

Environments:

  • CartPole
  • Lunar Lander

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This project provides a comprehensive understanding of reinforcement learning, focusing on Deep Q-Learning (DQN). It involves exploring the OpenAI Gym library, implementing DQN from DeepMind's seminal paper, and enhancing the DQN algorithm for improved performance and stability.

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reinforcement-learning deep-reinforcement-learning openai-gym deep-q-learning double-dqn

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