Reinforcement Learning Project with Gymnasium

Welcome to the Reinforcement Learning project designed as a sample for University of Hertfordshire students. This project demonstrates the basics of RL models using the gymnasium library.

Purpose

The purpose of this project is to provide a hands-on introduction to reinforcement learning concepts for students. It showcases the implementation of an RL model using the gymnasium environment, offering a practical insight into how RL algorithms can be applied to solve problems.

Key Components

Agent

The Agent in this project represents the learning entity that interacts with the environment. It employs a reinforcement learning algorithm to make decisions and learn from the consequences.

Environment

The Environment is the setting in which the agent operates. In this project, we use the gymnasium library to create RL environments, providing a standardized interface for defining tasks.

Action

Action refers to the moves or decisions that the agent can take in the environment. These actions influence the state of the environment and, consequently, the rewards received by the agent.

Q-function

The Q-function (Quality function) is a key concept in reinforcement learning. It estimates the expected future rewards of taking a particular action in a given state. The agent uses the Q-function to make decisions that maximize its cumulative reward over time.

Policy Function

The Policy function defines the strategy that the agent follows to select actions in different states. It can be deterministic or stochastic, specifying the probability distribution over actions given a state.

Getting Started

Follow these steps to set up and run the project:

1. Create a Conda Environment

# Create a conda environment (replace 'env' with your desired environment name)
conda create --name env python=3.8

# Activate the conda environment
conda activate env

2. Install Dependencies

Ensure you have Conda installed. Then, create the environment and install the required packages from environment.yml.

conda env create -f environment.yml

3. Run the Project

Execute the run.py file to start the RL model.

python run.py

This command will run the project, and you should see the RL model in action.

Additional Information

• This project uses the gymnasium library for RL environments.
• Feel free to explore and modify the code to experiment with different RL algorithms.

Happy coding!