for x in range(x_dims):
for y in range(y_dims):
utils[(x,y)] = 0
for state in problem.get_all_states():
utils[(state.x, state.y)] = state.reward # env.get_state_reward(state)
return utils
def find_policy_via_policy_iteration(problem,discount_factor):
policy = init_policy(problem)
return(policy)
if __name__ == "__main__":
# Initialize the maze environment
env = kuimaze.MDPMaze(map_image=GRID_WORLD3, probs=PROBS, grad=GRAD, node_rewards=GRID_WORLD3_REWARDS)
# env = kuimaze.MDPMaze(map_image=GRID_WORLD3, probs=PROBS, grad=GRAD, node_rewards=None)
# env = kuimaze.MDPMaze(map_image=MAP, probs=PROBS, grad=GRAD, node_rewards=None)
env.reset()
print('====================')
print('works only in terminal! NOT in IDE!')
print('press n - next')
print('press s - skip to end')
print('====================')
print(env.get_all_states())
# policy1 = find_policy_via_value_iteration(env)
policy = find_policy_via_policy_iteration(env,0.9999)
env.visualise(get_visualisation_values(policy))
env.render()
unchanged = True
return policy
def find_policy_via_value_iteration(problem, discount_factor, epsilon):
policy = init_policy(problem)
return policy
if __name__ == "__main__":
# Initialize the maze environment
# env = kuimaze.MDPMaze(map_image=GRID_WORLD3, probs=PROBS, grad=GRAD, node_rewards=GRID_WORLD3_REWARDS)
# env = kuimaze.MDPMaze(map_image=GRID_WORLD3, probs=PROBS, grad=GRAD, node_rewards=None)
env = kuimaze.MDPMaze(map_image=MAP,
probs=PROBS,
grad=GRAD,
node_rewards=None)
env.reset()
print('====================')
print('works only in terminal! NOT in IDE!')
print('press n - next')
print('press s - skip to end')
print('====================')
# agent = mdp_agent.MDP_agent(env)
# policy = mdp_agent.find_policy_via_policy_iteration(env, 0.1)
policy = mdp_agent.find_policy_via_value_iteration(env, 0.5, 0.0001)
env.visualise(get_visualisation_values(policy))
env.render()
print('Policy IV:', policy)