def visualise_agent(self, agent):
env = self.game_environment
display = Display(visible=0, size=(1400, 900))
display.start()
state = env.reset()
img = plt.imshow(env.render(mode='rgb_array'))
for t in range(1000):
agent.step()
img.set_data(env.render(mode='rgb_array'))
plt.axis('off')
display.display(env.gcf())
display.clear_output(wait=True)
if agent.done:
break
env.close()
import matplotlib.pyplot as plt
from pyvirtualdisplay import Display
# Set plotting options
plt.style.use('ggplot')
np.set_printoptions(precision=3, linewidth=120)
display = Display(visible=0, size=(1400, 900))
display.start()
plt.ion()
# Create an environment and set random sed
env = gym.make('MountainCar-v0')
env.seed(505)
state = env.reset()
img = plt.imshow(env.render(mode='rgb_array'))
for t in range(1000):
action = env.action_space.sample()
img.set_data(env.render(mode='rgb_array'))
plt.axis('off')
display.display(plt.gcf())
display.clear_output(wait=True)
state, reward, done, _ = env.step(action)
if done:
print('Score: ', t + 1)
break
env.close()