def visualize(self):
nr, nc = self.maze.shape
z = -0.1
a = self.agent
plot_line_seg(0, 0, z, nr, 0, z, 'e1', size=0.2, color='red')
plot_line_seg(0, 0, z, 0, nc, z, 'e2', size=0.2, color='red')
plot_line_seg(0, nc, z, nr, nc, z, 'e3', size=0.2, color='red')
plot_line_seg(nr, 0, z, nr, nc, z, 'e4', size=0.2, color='red')
plot_3d(*get_midpoint_for_loc(a.i, a.j), z, 'agent', color='blue', size=1)
plot_3d(*get_midpoint_for_loc(nr-1, nc-1), z, 'goal', color='green', size=1)
x, y = np.where(self.maze == WALL)
plot_3d(x + 0.5, y + 0.5, [z]*len(x), 'wall', color='red', size=1)
def visualize3d(self):
# First we need to check if it is :in bounds:
nr, nc = self.env.shape
z = 0.1
a = self.mousy
# plot_3d(x)
plot_line_seg(0, 0, z, nr, 0, z, 'e1', size=0.2, color='red')
plot_line_seg(0, 0, z, 0, nc, z, 'e2', size=0.2, color='red')
plot_line_seg(0, nc, z, nr, nc, z, 'e3', size=0.2, color='red')
plot_line_seg(nr, 0, z, nr, nc, z, 'e4', size=0.2, color='red')
plot_3d(*get_midpoint_for_loc(a.i, a.j),
z,
'mousy',
color='blue',
size=1)
plot_3d(*get_midpoint_for_loc(3, 3), z, 'goal', color='green', size=1)
xarr, yarr = np.where(self.env == -1)
plot_3d(xarr + 0.5, yarr + 0.5, [z] * len(xarr), 'obstacles', size=1.0)
def visualize(self):
nr, nc = self.env.shape
z = -0.1
a = self.mousy
plot_line_seg(0, 0, z, nr, 0, z, 'e1', size=0.2, color='red')
plot_line_seg(0, 0, z, 0, nc, z, 'e2', size=0.2, color='red')
plot_line_seg(0, nc, z, nr, nc, z, 'e3', size=0.2, color='red')
plot_line_seg(nr, 0, z, nr, nc, z, 'e4', size=0.2, color='red')
plot_3d(*get_midpoint_for_loc(a.i, a.j),
z,
'mousy',
color='blue',
size=1)
plot_3d(*get_midpoint_for_loc(nr - 1, nc - 1),
z,
'goal',
color='green',
size=1)
xarr, yarr = np.where(self.env == -1)
plot_3d(xarr + 0.5, yarr + 0.5, [z] * len(xarr), 'obstacles', size=1.0)
def main():
size = 8
q = QLearning(size**2, 4)
go_ahead = False
while not go_ahead:
m = make_test_maze(size)
m.visualize3d()
conti = input()
if conti.lower() == 'n':
continue
go_ahead = True
max_episodes = 400
switch_episodes = 200
for i in range(max_episodes):
m.reset()
print(i, end=" ")
final_score = 0
while not m.has_won():
# EXPLORATION VS EXPLOITATION
### Exploration
if random.random() > anneal_probability(
i, max_episodes, switch_episodes,
0.5) or i < switch_episodes:
# list me all the moves possible for the agent
moves = m.compute_possible_moves()
# shuffle the moves
random.shuffle(moves)
# move: tuple, move_idx: bottom(0), top(1), left (2), right(3)
move, move_idx = moves[0]
### Exploitation
else:
moves = m.all_actions
s = m.state_for_agent(m.mousy)
move_idx = np.argmax(q.q[s])
move = moves[move_idx]
at = move_idx
st = m.state_for_agent(m.mousy)
score = m.do_a_move(move)
final_score += score
rt = score
# print(score)
# lm = m.mousy() #last action of the agent
st1 = m.state_for_agent(m.mousy)
# m.visualize3d()
# time.sleep(0.001)
q.update(st, at, rt, st1)
time.sleep(0.01)
print(f"Finished episode with final score of {final_score} ")
print(q.q)
m.reset()
m.visualize3d()
agents = []
while not m.has_won():
time.sleep(0.5)
# Pick the state of the agent
s = m.state_for_agent(m.mousy)
# Select the action that has the highest action value
a_idx = np.argmax(q.q[s])
agents.append(m.mousy)
# Tell our agent to make a move
m.do_a_move(m.all_actions[a_idx])
m.visualize3d()
for ii, (a1, a2) in enumerate(zip(agents, agents[1:])):
plot_line_seg(a1.i + 0.5,
a1.j + 0.5,
0,
a2.i + 0.5,
a2.j + 0.5,
0,
f'path{ii}',
size=0.1)
m.visualize3d()
time.sleep(0.3)
m.reset()