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Maze-RL

Agent
Creates an agent to reward or penalize the robot for making specific moves at specific actions
Learns what actions need to be made through the DQN class
Epsilon decreases exponentially at a rate of 0.99995 (can be changed but a number above 0.99 is recommended for this simulation)

DQN
DQN algorithm
Calculates the correct action

Environment
Randomly generates a maze environment
The starting position is the top left corner and the destination is either a point on the right or the bottom edges

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