So I’ve just started playing around with the embedding policy. My previous agent was trained on memoization and the keras policies. Now I’ve decided to switch the Keras for the embedding policy but training is taking a lot longer to finish. Is this training policy expected to be much slower than the keras policy? I haven’t really went in depth to understand the architecture of each policy, but before I’d just like to know for now if this behavior is expected. I’m currently running tensorflow on a old rusty cpu but because i have a relatively small dataset, the keras policy would take less than five minutes to train. But now more than an hour and a half have passed and i’m still at 21% with the embedding policy. Is this normal?
This is my code to train the agent
agent = Agent(‘domain.yml’, policies=[MemoizationPolicy(max_history=4), EmbeddingPolicy()])
data = agent.load_data(training_data_file)
agent.train(data, augmentation_factor=10, epochs=200, batch_size=50, validation_split=0.2)