Deep Q-Learning with PyTorch

1. Overview:

This project implements a Deep Q-Learning approach using PyTorch. Deep Q-Learning combines Q-Learning with deep neural networks to solve complex decision-making tasks. The primary goal of this project is to train an agent capable of learning optimal policies through interactions with its environment.

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2. Features:

• Implements a Deep Q-Network (DQN) for reinforcement learning.
• PyTorch-based implementation for easy extensibility.
• Supports replay memory and target networks.
• Configurable hyperparameters for learning and exploration.

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3. How It Works:

1. Environment Interaction:
   The agent interacts with the environment to gather experiences in the form of states, actions, rewards, and next states.

2. Replay Memory:
   Experiences are stored in a memory buffer to decorrelate data and improve training stability.

3. Deep Q-Network:
   A neural network estimates the Q-value for each state-action pair.

4. Optimization:
   The network is optimized using stochastic gradient descent to minimize the difference between predicted and target Q-values.

5. Target Network:
   A separate network is used to calculate stable target Q-values during training.

6. Exploration vs. Exploitation:
   An epsilon-greedy policy balances exploration of new actions with exploitation of the learned policy.

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4. Prerequisites:

• Python 3.8+
• PyTorch
• NumPy
• Matplotlib
• OpenAI Gym (if applicable for environment setup)

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5. Setup Instructions:

1. Clone the repository:

   git clone https://github.com/Prasannakumar/Trader-Bot-Deep-Reinforcement-Learning.git
   cd Trader-Bot-Deep-Reinforcement-Learning

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the Jupyter notebook:

   jupyter notebook deep_q_torch.ipynb

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6. Usage:

• Open the notebook and execute each cell sequentially.
• Modify the hyperparameters in the configuration section to experiment with different learning rates, discount factors, and exploration rates.

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7. Code Structure:

• Setup Section: Initializes libraries and configures the environment.
• Replay Memory Class: Handles experience storage and sampling.
• Deep Q-Network Class: Implements the neural network for Q-value estimation.
• Training Loop: Iteratively trains the agent using collected experiences.
• Evaluation Section: Tests the trained agent in the environment and visualizes performance.

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8. Results:

• A trained agent capable of solving tasks in the given environment.
• Visualization of reward progression during training.
• Performance evaluation metrics.

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9. Contributing:

Contributions are welcome! Please submit a pull request or open an issue for discussion.

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10. License:

This project is open-source and freely available for all. Be mindful of the terms and conditions of OpenAI and other technologies used within this application.