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pd.DataFrame(list(zip(transaction_data_only.columns[2:], tuned_rf_model.feature_importances_)), \
             columns=['Attribute', 'Feature Importance']).sort_values(by='Feature Importance', ascending=False)

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state, dqn_agent = env.reset(train_mode=True)[brain_name].vector_observations[0], Agent(state_size, action_size, 1024);

scores, discount = [], EPS;

for ite in range(1, num_iterations+1):
    score, env_info = 0, env.reset(train_mode=True)[brain_name];
    state = env_info.vector_observations[0];
    
    for t_step in range(max_timesteps):
        action = dqn_agent.act(state, discount);

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class Agent():
    """Interacts with and learns from the environment."""

    def __init__(self, state_size, action_size, replay_memory, batch_size, random_seed):
        """Initialize an Agent object.
        - Instantiate the Agents and Critics, Replay Memory, and a Noise process
        """


    def step(self, state, action, reward, next_state, done):

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def multi_ddpg(n_episodes=5000, max_t=2000):
    init_time = time.time();
    scores_deque = deque(maxlen=100);
    scores = []
    max_score = -np.Inf;
    
    for i_episode in range(1, n_episodes+1):
        ep_init_time = time.time();
        env_info = env.reset(train_mode=True)[brain_name];
        states = env_info.vector_observations;

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{
    "openapi":"3.0.1",
    "info":{
       "title":"The Jira Cloud platform REST API",
       "description":"Jira Cloud platform REST API documentation",
       "termsOfService":"http://atlassian.com/terms/",
       "contact":{
          "email":"[email protected]"
       },
       "license":{

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def check_coverage(text, embeddings_dict):
    known_words, unknown_words = {}, {};
    total_known, total_unknown = 0, 0;
    
    for sentence in text:
        for word in sentence.split(' '):
            if word in known_words:
                total_known = total_known + 1;
            elif word in embeddings_dict:
                known_words[word] = embeddings_dict[word];

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