Week 3 homework, Part I: On-policy learning and SARSA

[afakharany93]

I face an error from pandas when ever I run the cell containing
`from IPython.display import clear_output
from pandas import ewma, Series

moving_average = lambda ts, span=100: ewma(Series(ts), min_periods=span//10, span=span).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), label='ev_sarsa')
    plt.plot(moving_average(rewards_ql), label='qlearning')
    plt.grid()
    plt.legend()
    plt.ylim(-500, 0)
    plt.show()` 

the error message is :
`ImportError Traceback (most recent call last)
in ()
1 from IPython.display import clear_output
----> 2 from pandas import ewma, Series
3 # from pandas import Dataframe.ewm as ewm
4 moving_average = lambda ts, span=100: ewma(Series(ts), min_periods=span//10, span=span).values
5

ImportError: cannot import name 'ewma'
`

I tried googling it but I can't find a fix for it.

using python 3.5 and pandas 0.23.0 on ubuntu 16.04

[justheuristic]

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

[afakharany93]

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()

`