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Reinforcement Learning
Let us now instantiate our agent for the CartPole environment and train it:
env_string = 'CartPole-v0'
agent = DQN(env_string)
scores = agent.train()
In the following screenshot you can see the agent being trained on my system. The
agent was able to achieve our set threshold of 45 in 254 steps:
Figure 2: Agent training for the CartPole environment, achieving the target treshold within 254 steps
And the average reward plot as the agent learns is:
import matplotlib.pyplot as plt
plt.plot(scores)
plt.show()
Figure 3: Average agent reward plot
Once the training is done you can close the environment:
agent.env.close()
You can see starting from no information about how to balance the pole, the agent
using DQN is able to balance the pole for more and more time (on average) as
it learns. Starting from the blank state, the agent is able to build information/
knowledge to fulfill the required goal. Remarkable!
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