<jupyter_start><jupyter_text>Without breaks<jupyter_code>n_users = 100
n_items = 30
random.seed(123)
user = random.randint(0, n_users)
forget_rates, repetition_rates = generate_agents(n_users, n_items)
print("forget", forget_rates[user].mean())
print("repeat", repetition_rates[user].mean())
interval = 100
fig, axs = plt.subplots(1, 2)
fig.suptitle('coeff = {}, forget = {}, repeat = {}'.
             format(interval, forget_rates[user].mean(), repetition_rates[user].mean()))

for i, multiply_delta in enumerate([False, True]):
    env = ContinuousTeaching(
        t_max=100,
        initial_forget_rates=forget_rates[user],
        initial_repetition_rates=repetition_rates[user],
        n_item=n_items,
        interval=interval,
        multiply_delta=multiply_delta,
        tau=0.9)
    
    model = A2C(env, seed=123)

    iterations = env.t_max * 1000
    check_freq = env.t_max

    with ProgressBarCallback(env, check_freq) as callback:
        model.learn(iterations, callback=callback)
        axs[i].plot([np.mean(r) for r in callback.hist_rewards])
        axs[i].set_title('{}'.format(multiply_delta))
plt.plot([np.mean(r) for r in callback.hist_rewards])<jupyter_output><empty_output>
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