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Reinforcement Learning
env_name = 'Breakout-v0'
env = gym.make(env_name)
Let's get an observation of the environment once it is reset:
obs = env.reset()
env.render()
You can see the Breakout environment in the following screenshot; the render
function pops up the environment window:
The Breakout environment
Alternatively, you can use Matplotlib inline and change the render command to plt.
imshow(env.render(mode='rgb_array')). This will show the environment inline
in the Jupyter Notebook.
You can learn more about the environment state space and its action space using
env.observation_space and env.action_space. For our Breakout game we find
that the state consists of a three-channel image of size 210 × 160, and the action space
is discrete with four possible actions. Once you are done, do not forget to close the
OpenAI using:
env.close()
Random agent playing Breakout
Let's have some fun and play the Breakout game. When I first played the game, I had
no idea of the rules, or how to play, so I randomly chose the control buttons. Our
novice agent will do the same; it will choose the actions randomly from the action
space. Gym provides a function sample() which chooses a random action from
the action space – we will be using this function. Also, we can save a replay of the
game, to view it later. There are two ways to save the play, one using Matplotlib and
another using OpenAI Gym Monitor wrapper. Let us first see the Matplotlib method.
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