https://github.com/zazi2002/neuroscience-reinforcement-learning

Neuroscience of Learning, Memory, Cognition project, containing a Jupyter notebook implementing a reinforcement learning approach for a neuroscience-related task.
https://github.com/zazi2002/neuroscience-reinforcement-learning

deep-learning matplotlib numpy pytorch reinforcement-learning

Last synced: 10 days ago
JSON representation

Neuroscience of Learning, Memory, Cognition project, containing a Jupyter notebook implementing a reinforcement learning approach for a neuroscience-related task.

• Host: GitHub
• URL: https://github.com/zazi2002/neuroscience-reinforcement-learning
• Owner: ZaZi2002
• Created: 2024-09-06T16:02:09.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-06T16:06:49.000Z (4 months ago)
• Last Synced: 2024-11-07T13:18:46.412Z (2 months ago)
• Topics: deep-learning, matplotlib, numpy, pytorch, reinforcement-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 104 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Neuroscience - Reinforcement Learning

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.

## Project Overview

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.

## Files

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

## Key Concepts

- **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.

## Requirements

- Python 3.8 or higher

- TensorFlow or PyTorch (depending on the framework used in the notebook)

- NumPy

- Matplotlib

- Jupyter Notebook