SmartCV AI CV Analyzer

Project Description

The Q-Learning Grid World Agent is a reinforcement learning project where an autonomous agent learns how to navigate a 5×5 grid world containing obstacles, sparse rewards, penalties, and a special jump transition. The agent uses Q-learning and ε-greedy exploration to learn an optimal policy through trial and error. Your uploaded paper defines this as a 5×5 grid problem with obstacles, a terminal reward, a jump reward, step penalties, and experiments using different epsilon values.

Problem

The challenge is to train an agent to reach a goal state efficiently while avoiding obstacles and minimising unnecessary steps. The agent must balance exploration and exploitation: exploring unknown paths while gradually exploiting the best learned route.

Solution

The solution uses a Q-table where each state-action pair is updated using the Q-learning update rule. Different ε values are compared to evaluate how exploration affects convergence and total reward. The best-performing configuration is used to derive the final greedy policy.

Main Features

FeatureDescription

Grid Environment

5×5 state space

Obstacles

Impassable states that force route planning

Reward System

Step penalty, terminal reward and jump reward

Q-table

Stores learned action values

ε-greedy Policy

Controls exploration vs exploitation

Training Graphs

Shows reward progression and episode steps

Policy Visualisation

Shows final optimal direction from each state

Documentation Angle

This is one of your strongest academic projects. It should be documented as a reinforcement learning experimental paper with sections for problem formulation, Bellman equation, Q-table construction, training process, hyperparameter tuning, results, convergence analysis and limitations.