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Implement a basic reinforcement learning algorithm (e.g., Q-learning) to solve a simple problem (e.g., a grid world). Submit your code AND a separate 2-3-page report on your approach and results.Your...

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Answered 1 days After Sep 10, 2024

Solution

Robert answered on Sep 11 2024
8 Votes
3
Solving GridWorld with QLearning: A Reinforcement Learning Approach
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Introduction
Reinforcement Learning is a machine learning paradigm where an agent learns to take actions in an environment to maximize cumulative rewards. In this project, the Q-learning algorithm is applied to solve a simple GridWorld Navigation problem. The agent must learn to navigate from the start point to a goal while avoiding obstacles, receiving rewards for reaching the goal, and penalties for hitting obstacles (Sutton et.al, 2018).
The GridWorld is a 5*5 grid, with a start at the top-left corner and a goal at the bottom-right corner. The agent can move up, down, left, and right to encounter obstacles that penalize its moment.
Algorithm Description Paragraph Formatting
Q-learning is an off-policy learning algorithm that learns the value of state-action pairs (Q-values) through...
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