Question

Consider the following environment of PacMan
For

For the environment design a Reinforcement Learning Agent (Pacman), the objective of the agent is to figure out the best actions the agent can take at any given state.
The rules of the game are as follows:
 Every move has a reward of -1
 Consuming a food pellet will have a reward of +10
 If pacman collides with a ghost, then the reward will be -500
 If the pacman has eaten all the food pellets without colliding with the ghosts, then the reward will be +500
 Assume a discount factor of 0.8
 The action noise is 0.3 (the consequences are the same as in the grid world example)
 The environment is static i.e. no ghosts are moving
 The actions for pacman are Up, Down, North and Right
 You can cross the walls
Use Q-Learning to figure out the best action at every state. Show your working for every iteration of Q-Learning.

Answer 1

Answer:

Nice Game nothing