pdfcoffee

Chapter 11
We will first import the necessary modules; we will only need gym and matplotlib
for now, as the agent will be playing random moves:
import gym
import matplotlib.pyplot as plt
import matplotlib.animation as animation
We create the Gym environment:
env_name = 'Breakout-v0'
env = gym.make(env_name)
Next we will run the game, one step at a time, choosing a random action, either for
300 steps or until the game is finished (whichever is earlier). The environment state
(observation) space is saved at each step in the list frames:
frames = [] # array to store state space at each step
env.reset()
done = False
for _ in range(300):
#print(done)
frames.append(env.render(mode='rgb_array'))
obs,reward,done, _ = env.step(env.action_space.sample())
if done:
break
Now comes the part of combining all the frames as a gif image using Matplotlib
Animation. We create an image object, patch, and then define a function that sets
image data to a particular frame index. The function is used by the Matplotlib
Animation class to create an animation, which we finally save in the file random_
agent.gif:
patch = plt.imshow(frames[0])
plt.axis('off')
def animate(i):
patch.set_data(frames[i])
anim = animation.FuncAnimation(plt.gcf(), animate, \
frames=len(frames), interval=10)
anim.save('random_agent.gif', writer='imagemagick')
Normally, a RL agent requires lots of steps for proper training, and as a result it
is not feasible to store the state space at each step. Instead, we can choose to store
after every 500th step (or any other number you wish) in the preceding algorithm.
OpenAI Gym provides the Wrapper class to save the game as a video. To do so, we
need to first import wrappers, then create the environment, and finally use Monitor.
[ 419 ]