What is a reinforcement learning algorithm in C and how is it implemented?

Table of Contents

• Introduction
• Understanding Reinforcement Learning
  • Key Concepts
  • Learning Process
• Implementing Q-Learning in C
  • 1. Q-Learning Overview
  • 2. Q-Learning Implementation
  • Explanation of the Implementation
• Conclusion

Introduction

Reinforcement Learning (RL) is a branch of machine learning where an agent learns to make decisions by interacting with an environment to achieve the highest cumulative reward. This article explains the key concepts of RL and provides a simple implementation in C.

Understanding Reinforcement Learning

Key Concepts

1. Agent: The learner or decision-maker.
2. Environment: The context or space where the agent operates.
3. State: The current situation of the agent within the environment.
4. Action: A choice made by the agent that affects its environment.
5. Reward: Feedback received after performing an action, indicating the effectiveness of that action.

Learning Process

The agent explores its environment, makes decisions, and receives rewards. The objective is to learn a policy that maximizes long-term rewards.

Implementing Q-Learning in C

1. Q-Learning Overview

Q-learning is a popular model-free reinforcement learning algorithm that learns the value of actions in different states. It maintains a Q-table to store expected future rewards for each action in each state.

2. Q-Learning Implementation

Here’s a simple implementation of Q-learning in C:

Explanation of the Implementation

1. Global Variables:
   • qTable: A 2D array storing the Q-values for each state-action pair.
2. selectAction Function:
   • Implements the epsilon-greedy strategy to either explore or exploit based on the Q-values.
3. updateQTable Function:
   • Updates the Q-values using the Q-learning formula based on the received reward and estimated future rewards.
4. Main Function:
   • Initializes the Q-table.
   • Runs multiple episodes of the learning process, where the agent selects actions, receives rewards, and updates the Q-table accordingly.

Conclusion

Reinforcement Learning (RL) is a powerful method for training agents to make informed decisions based on feedback from their environment. This simple Q-learning implementation in C demonstrates the foundational concepts of RL and can be expanded to more complex scenarios. Future enhancements may include using function approximation for larger state spaces or implementing deep Q-learning techniques.