def test_env_manager_state(self):
frame_stack_size = 5
env = FrameStackEnvManager('Breakout-v0',
preprocess=preprocess,
frame_stack_size=frame_stack_size)
env.reset()
state = env.state()
self.assertEqual(frame_stack_size, state.shape[0])
def test_env_manager_reset(self):
env = FrameStackEnvManager('Pong-v0',
preprocess=preprocess,
frame_stack_size=4)
state = env.reset()
screen = torch.tensor(preprocess(env.render('rgb_array'))).float()
# All frames in the stack is similar
self.assertTrue(torch.equal(state[0], screen))
self.assertTrue(torch.equal(state[1], screen))
self.assertTrue(torch.equal(state[2], screen))
self.assertTrue(torch.equal(state[3], screen))
def test_env_manager_init(self):
stack_size = 3
env = FrameStackEnvManager('Breakout-v0',
preprocess=preprocess,
frame_stack_size=stack_size)
self.assertEqual(0, len(env.frames))
env.reset()
self.assertEqual(env.state()[0].shape, torch.Size(img_size))
self.assertEqual(env.state().shape[0], stack_size)
def test_env_manager_replay_memory(self):
ray.init()
memory = ReplayMemory()
env = FrameStackEnvManager('Pong-v0',
preprocess=preprocess,
replay_memory=memory)
env.reset()
for i in range(10):
env.step(env.action_space.sample())
self.assertEqual(len(memory.memory), 10)
ray.shutdown()
def test_env_manager_step(self):
env = FrameStackEnvManager('Pong-v0', preprocess=preprocess)
start_state = env.reset()
next_state, _, _, _ = env.step(env.action_space.sample())
new_frame = env.env.render('rgb_array')
new_frame = cv2.cvtColor(new_frame, cv2.COLOR_RGB2GRAY)
new_frame = cv2.resize(new_frame,
img_size,
interpolation=cv2.INTER_NEAREST)
new_frame = torch.tensor(new_frame).float()
self.assertTrue(torch.equal(next_state[0], start_state[1]))
self.assertTrue(torch.equal(next_state[1], start_state[2]))
self.assertTrue(torch.equal(next_state[2], start_state[3]))
self.assertTrue(torch.equal(next_state[3], new_frame))
next_state2, _, _, _ = env.step(env.action_space.sample())
new_frame = env.env.render('rgb_array')
new_frame = cv2.cvtColor(new_frame, cv2.COLOR_RGB2GRAY)
new_frame = cv2.resize(new_frame,
img_size,
interpolation=cv2.INTER_NEAREST)
new_frame = torch.tensor(new_frame).float()
self.assertTrue(torch.equal(next_state2[0], next_state[1]))
self.assertTrue(torch.equal(next_state2[1], next_state[2]))
self.assertTrue(torch.equal(next_state2[2], next_state[3]))
self.assertTrue(torch.equal(next_state2[3], new_frame))
next_state3, _, _, _ = env.step(env.action_space.sample())
next_state4, _, _, _ = env.step(env.action_space.sample())
new_frame = env.env.render('rgb_array')
new_frame = cv2.cvtColor(new_frame, cv2.COLOR_RGB2GRAY)
new_frame = cv2.resize(new_frame,
img_size,
interpolation=cv2.INTER_NEAREST)
new_frame = torch.tensor(new_frame).float()
self.assertTrue(torch.equal(next_state4[0], next_state[3]))
self.assertTrue(torch.equal(next_state4[1], next_state2[3]))
self.assertTrue(torch.equal(next_state4[2], next_state3[3]))
self.assertTrue(torch.equal(next_state4[3], new_frame))
f'./resources/{env_id.split(":")[1]}_crowding_heatmap.png')
# Last gen position
step = max(1, int((num_generations - 1) / 4))
for i in range(0, num_generations, step):
bg_img = f'{env_id.split(":")[1].split("-")[1]}'
last_gen_pos = positions_log[i * (num_populations * 2):(i + 1) *
(num_populations * 2)]
last_gen_heatmap_plot, ax = plot_final_pos(
cv.resize(cv.imread(f'./resources/{bg_img}.png', 0), (48, 48)),
last_gen_pos)
last_gen_heatmap_plot.savefig(
f'./resources/{env_id.split(":")[1]}_crowding_{int(i/step)}_pos.png')
controller = solution.to_network()
torch.save(controller.state_dict(),
f'./resources/{env_id.split(":")[1]}_crowding.params')
env = FrameStackEnvManager(env_id, frame_stack_size=1, preprocess=preprocess)
for i in range(2):
total_reward = 0
state = env.reset()
done = False
while not done:
env.render()
action = controller(state.unsqueeze(0)).argmax().item()
state, reward, done, _ = env.step(action)
total_reward += reward.item()
print('Total reward : ', total_reward)
import numpy as np
from environment.framestack_env_manager import FrameStackEnvManager
from environment.classic_control_env_manager import ClassicControlEnvManager
img_size = (64, 64)
#%% Test EnvironmentManager
def preprocess(screen):
screen = cv2.cvtColor(screen, cv2.COLOR_RGB2GRAY)
screen = cv2.resize(screen, (64, 64), interpolation=cv2.INTER_NEAREST)
screen = np.ascontiguousarray(screen)
return screen
env = FrameStackEnvManager('Breakout-v0', preprocess=preprocess)
raw_screen = env.get_raw_screen()
plt.figure()
plt.title('Raw Screen')
plt.axis('off')
plt.imshow(raw_screen) #.transpose(1,2,0))
plt.show()
env.reset()
state = env.state()
plt.suptitle('Initial State')
plt.subplot(141)
plt.axis('off')
plt.imshow(state[0], cmap=plt.get_cmap('gray'))
plt.subplot(142)