def trial(robot: Agent) -> List[int]:
maze = Maze()
move_history = []
for i in range(5000):
if i % 1000 == 0:
print(i)
while not maze.is_complete():
state, _ = maze.get_state_and_reward()
action = robot.choose_action(state, maze.allowed_states[state])
maze.update_maze(action)
state, reward = maze.get_state_and_reward()
robot.update_state_history(state, reward)
if maze.steps > 1000:
maze.robot_position = State(5, 5)
robot.learn()
move_history.append(maze.steps)
maze.reset()
return move_history
import numpy as np
from environment import Maze
from agent import Agent
from constants import maze_configuration
if __name__ == '__main__':
maze = Maze(maze_configuration)
robot = Agent(maze.allowed_states, alpha=0.1, exploration_factor=0.25)
move_history = []
robot.printRewardMap()
for episode in range(5000):
if episode % 1000 == 0:
print(episode)
robot.printRewardMap()
while not maze.isGameOver():
state, _ = maze.getStateAndReward()
action = robot.chooseAction(state, maze.allowed_states[state])
maze.updateMaze(action)
state, reward = maze.getStateAndReward()
robot.updateStateHistory(state, reward)
if maze.steps > 1000:
maze.robot_position = (5, 5)
robot.learn()
move_history.append(maze.steps)
maze = Maze(maze_configuration)
