Week 3 homework, Part I: On-policy learning and SARSA about practical_rl HOT 2 CLOSED

THat's our mistake, we relied on a deprecated function from earlier pandas versions. Try this:

from pandas import DataFrame
moving_average = lambda x, span: DataFrame({'x':np.asarray(x)}).x.ewm(span=span).mean().values

We've also updated it on github

from practical_rl.

that worked, but a span value should be passed to the moving_average lambda function, here is the cell code that made it work
`
from IPython.display import clear_output
from pandas import DataFrame
moving_average = lambda x, span: DataFrame({'x':np.asarray(x)}).x.ewm(span=span).mean().values

rewards_sarsa, rewards_ql = [], []

for i in range(5000):
rewards_sarsa.append(play_and_train(env, agent_sarsa))
rewards_ql.append(play_and_train(env, agent_ql))
#Note: agent.epsilon stays constant

if i %100 ==0:
    clear_output(True)
    print('EVSARSA mean reward =', np.mean(rewards_sarsa[-100:]))
    print('QLEARNING mean reward =', np.mean(rewards_ql[-100:]))
    plt.title("epsilon = %s" % agent_ql.epsilon)
    plt.plot(moving_average(rewards_sarsa,100), label='ev_sarsa')
    plt.plot(moving_average(rewards_ql,100), label='qlearning')
    plt.grid()
    plt.legend()
    plt.ylim(-500, 0)
    plt.show()

`

from practical_rl.