def exp(name='or',
save_trajectories=True,
max_episodes=4,
max_trajectory=7):
env = CollectEnv()
dqn_blue = load('./models/blue/model.dqn', env)
dqn_crate = load('./models/crate/model.dqn', env)
if torch.cuda.is_available():
dqn_blue.cuda()
dqn_crate.cuda()
dqn_not = ComposedDQN([dqn_blue], compose="not")
dqn_or = ComposedDQN([dqn_blue, dqn_crate], compose="or")
dqn_and = ComposedDQN([dqn_blue, dqn_crate], compose="and")
dqn_not_and = ComposedDQN([dqn_and], compose="not")
dqn_xor = ComposedDQN([dqn_or, dqn_not_and], compose="and")
goals = []
if os.path.exists('./goals.h5'):
goals = dd.io.load('goals.h5')
if name == 'blue':
dqn = dqn_blue
goal_condition = lambda x: x.colour == 'blue'
elif name == 'square':
dqn = dqn_crate
goal_condition = lambda x: x.shape == 'square'
elif name == 'not':
dqn = dqn_not
goal_condition = lambda x: not x.colour == 'blue'
elif name == 'or':
dqn = dqn_or
goal_condition = lambda x: x.colour == 'blue' or x.shape == 'square'
elif name == 'and':
dqn = dqn_and
goal_condition = lambda x: x.colour == 'blue' and x.shape == 'square'
elif name == 'xor':
dqn = dqn_xor
goal_condition = lambda x: (
x.colour == 'blue' or x.shape == 'square') and not (
x.colour == 'blue' and x.shape == 'square')
# else:
# print("Invalid name")
# return
# env = MaxLength(WarpFrame(CollectEnv(start_positions=start_positions,goal_condition=lambda x: True)), max_trajectory)
poss = [(3, 4), (1, 2), (5, 7), (5, 2)]
trajectories = []
with torch.no_grad():
episode = 0
while episode < max_episodes:
start_positions['crate_beige'] = poss[episode]
env = (WarpFrame(
CollectEnv(start_positions=start_positions,
changePlayerPos=False,
goal_condition=lambda x: True)))
obs = env.reset()
trajectory = []
for _ in range(max_trajectory):
trajectory.append(
Image.fromarray(np.uint8(env.render(mode='rgb_img'))))
obs = torch.from_numpy(obs).type(FloatTensor).unsqueeze(0)
values = []
for goal in goals:
goal = torch.from_numpy(
np.array(goal)).type(FloatTensor).unsqueeze(0)
x = torch.cat((obs, goal), dim=3)
values.append(dqn_blue(x).squeeze(0))
values = torch.stack(values, 1).t()
valuesb = values.data.max(0)[0]
values = []
for goal in goals:
goal = torch.from_numpy(
np.array(goal)).type(FloatTensor).unsqueeze(0)
x = torch.cat((obs, goal), dim=3)
values.append(dqn_crate(x).squeeze(0))
values = torch.stack(values, 1).t()
valuess = values.data.max(0)[0]
values = torch.stack((valuesb, valuess), 0).min(0)[0]
action = values.max(0)[1].item()
obs, reward, done, _ = env.step(action)
if done:
break
trajectories += trajectory[:-1]
episode += 1
if save_trajectories:
trajectories[0].save('./trajectories/' + name + '.gif',
save_all=True,
append_images=trajectories[1:],
optimize=False,
duration=250,
loop=0)
def exp(name='or',
save_trajectories=True,
max_episodes=4,
max_trajectory=20):
env = CollectEnv()
dqn_purple = load('./models/purple/model.dqn', env)
dqn_blue = load('./models/blue/model.dqn', env)
dqn_crate = load('./models/crate/model.dqn', env)
if torch.cuda.is_available():
dqn_purple.cuda()
dqn_blue.cuda()
dqn_crate.cuda()
dqn_max = ComposedDQN([dqn_purple, dqn_blue, dqn_crate], compose="or")
dqn_not = ComposedDQN([dqn_blue], dqn_max=dqn_max, compose="not")
dqn_or = ComposedDQN([dqn_blue, dqn_crate], compose="or")
dqn_and = ComposedDQN([dqn_blue, dqn_crate], compose="and")
dqn_not_and = ComposedDQN([dqn_and], dqn_max=dqn_max, compose="not")
dqn_xor = ComposedDQN([dqn_or, dqn_not_and], compose="and")
goals = []
if os.path.exists('./goals.h5'):
goals = dd.io.load('goals.h5')
if name == 'blue':
dqn = dqn_blue
goal_condition = lambda x: x.colour == 'blue'
elif name == 'purple':
dqn = dqn_purple
goal_condition = lambda x: x.colour == 'purple'
elif name == 'square':
dqn = dqn_crate
goal_condition = lambda x: x.shape == 'square'
if name == 'not':
dqn = dqn_not
goal_condition = lambda x: not x.colour == 'blue'
elif name == 'or':
dqn = dqn_or
goal_condition = lambda x: x.colour == 'blue' or x.shape == 'square'
elif name == 'and':
dqn = dqn_and
goal_condition = lambda x: x.colour == 'blue' and x.shape == 'square'
elif name == 'xor':
dqn = dqn_xor
goal_condition = lambda x: (
x.colour == 'blue' or x.shape == 'square') and not (
x.colour == 'blue' and x.shape == 'square')
else:
print("Invalid name")
return
env = MaxLength(WarpFrame(CollectEnv(goal_condition=goal_condition)),
max_trajectory)
trajectories = []
with torch.no_grad():
episode = 0
while episode < max_episodes:
obs = env.reset()
trajectory = []
for _ in range(max_trajectory):
trajectory.append(
Image.fromarray(np.uint8(env.render(mode='rgb_img'))))
obs = torch.from_numpy(obs).type(FloatTensor).unsqueeze(0)
values = []
for goal in goals:
goal = torch.from_numpy(
np.array(goal)).type(FloatTensor).unsqueeze(0)
x = torch.cat((obs, goal), dim=3)
values.append(dqn(x).squeeze(0))
values = torch.stack(values, 1).t()
action = values.data.max(0)[0].max(0)[1].item()
obs, reward, done, _ = env.step(action)
if done:
episode += 1
trajectories += trajectory[:-1]
break
if save_trajectories:
trajectories[0].save('./trajectories/' + name + '.gif',
save_all=True,
append_images=trajectories[1:],
optimize=False,
duration=250,
loop=0)