def select_optimal_path(q_table, enviroment, state_evaluate):
global steps, steps_desc
i, j = identifies_state(enviroment, enviromentsize)
k, l = identifiesgoal_state(enviroment, enviromentsize)
state = int(state_matrix[i][j])
goal_state = int(state_matrix[k][l])
states = []
steps = []
steps_desc = []
states.append(state)
reward, next_state = 0, 0
done = False
while (not done):
print(state)
action = select_optimal_action(state)
reward, next_state = next_step(action, state, goal_state)
state = next_state
states.append(state)
if reward == 10:
done = True
states = states[:-1]
define_steps()
print(q_table, '\n', '\n', states, '\n', steps, '\n', steps_desc, '\n',
'path_length:', len(states))
plot_q_with_steps(enviroment, states, enviromentsize, state_evaluate)
steps_matrix.append(steps_desc)
def select_optimal_path(q_table, enviroment):
enviroment[0][0] = 1
# reset enviroment to learn a new goal
i, j = identifies_state(enviroment, enviromentsize)
k, l = identifiesgoal_state(enviroment, enviromentsize)
state = int(state_matrix[i][j])
goal_state = int(state_matrix[k][l])
states = []
states.append(state)
print(state, goal_state, '\n')
print('\n', q_table, '\n', enviroment)
old_state, reward, next_state = 0, 0, 0
while(int(reward) < 20):
action = select_optimal_action(q_table, state)
reward, next_state = next_step(
q_table, enviroment, action, state, old_state, goal_state)
old_state = state
state = next_state
states.append(state)
print(states)
print(state, goal_state, '\n', enviroment)
print(states, '\n', q_table)
