aicarsimulator-model-

Here's a README file for your Deep Q Learning implementation:

Deep Q Learning

This repository contains an implementation of Deep Q Learning using PyTorch. The code includes a neural network for approximating Q-values, experience replay for training stability, and mechanisms for saving and loading model checkpoints.

Overview

Components

1. Neural Network (Network)

   • A simple feedforward neural network with one hidden layer.
   • Input layer to hidden layer: 30 neurons.
   • Hidden layer to output layer: Number of actions.

2. Experience Replay (ExperienceReplay)

   • Stores and manages past experiences to improve training stability.
   • Allows sampling of random batches of experiences for training.

3. Deep Q Learning (DeepQNetwork)

   • Manages training and decision-making processes.
   • Utilizes the neural network and experience replay to learn optimal policies.
   • Includes methods for action selection, learning from experiences, and model saving/loading.

Installation

Ensure you have the required libraries installed:

bash pip install numpy torch

Usage

1. Initialize the Deep Q Network:

python dqn = DeepQNetwork(input_size=4, number_of_actions=2, gamma=0.99)

• input_size: Number of features in the state.
• number_of_actions: Number of possible actions.
• gamma: Discount factor for future rewards.

1. Updating the Model:

• Call update(reward, new_signal) after each step to update the model with new experiences.

python action = dqn.update(reward=1.0, new_signal=[1, 0, 0, 1])

1. Saving and Loading Models:

• To save the model:

  python dqn.save()

• To load a saved model:

  python dqn.load()

1. Scoring the Model:

• To get the average score:

  python average_score = dqn.score()

Code Overview

• Network Class: Defines the neural network architecture.
• ExperienceReplay Class: Manages the replay buffer.
• DeepQNetwork Class: Implements Deep Q Learning algorithm with methods for action selection, learning, and model management.

Example Code

Here's a snippet showing how to use the DeepQNetwork class:

```python import torch

Initialize DeepQNetwork

dqn = DeepQNetwork(inputsize=4, numberof_actions=2, gamma=0.99)

Simulate an experience

reward = 1.0 new_signal = [1, 0, 0, 1]

Update the model

action = dqn.update(reward=reward, newsignal=newsignal)

Save the model

dqn.save()

Load the model

dqn.load()

Get the average score

averagescore = dqn.score() print(f"Average Score: {averagescore}") ```

License

This project is open-source and free to use. You may use, modify, and distribute it as you see fit.