The agent learns how to navigate a maze (defined in Gridworld.py) via an RL based value iteration algorithm. The above is an example of a Markov Decision Process. One may edit the maze to their liking by changing the rewards and obstacle coordinates.
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The agent learns how to navigate a maze (defined in Gridworld.py) via an RL based value iteration algorithm. The above is an example of a Markov Decision Process. One may edit the maze to their liking by changing the rewards and obstacle coordinates.
axe76/RL-based-Maze-Solver
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The agent learns how to navigate a maze (defined in Gridworld.py) via an RL based value iteration algorithm. The above is an example of a Markov Decision Process. One may edit the maze to their liking by changing the rewards and obstacle coordinates.
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