This repository contains a Jupyter notebook implementing a reinforcement learning approach for a neuroscience-related task. The project demonstrates how neural networks and reinforcement learning can be applied to specific neuroscience problems to achieve efficient learning outcomes.

The primary goal of this project is to develop and evaluate a reinforcement learning model capable of interacting with an environment to learn and improve its performance over time. The project focuses on the following concepts:

• Deep Q-Learning (DQN): Implementing a DQN agent to interact with the environment and optimize rewards.
• Target Q-Network: Introducing a target Q-network to stabilize training by predicting target Q-values via regression.
• Exploration vs Exploitation: Balancing exploration and exploitation during training.
• Performance Metrics: Tracking the score and performance of the agent across multiple episodes.
• Optimization: Fine-tuning hyperparameters to achieve better learning performance.

• Neuroscience_Project_99101705.ipynb: Main Jupyter notebook containing the reinforcement learning code for the project.

• Q-Learning: A model-free reinforcement learning algorithm to find the best action to take given the current state.
• DQN: Deep Q-Network, a neural network used to approximate the Q-value function.
• Replay Buffer: Storing experiences from which the agent samples to improve learning efficiency.
• Target Network: A secondary neural network used to predict more stable Q-values for updating the main network.

• Python 3.8 or higher
• TensorFlow or PyTorch (depending on the framework used in the notebook)
• NumPy
• Matplotlib
• Jupyter Notebook