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Deep Q-learning from Demonstrations Artificial Intelligence

Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor. This may be acceptable for a simulator, but it severely limits the applicability of deep RL to many real-world tasks, where the agent must learn in the real environment. In this paper we study a setting where the agent may access data from previous control of the system. We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism. DQfD works by combining temporal difference updates with supervised classification of the demonstrator's actions. We show that DQfD has better initial performance than Prioritized Dueling Double Deep Q-Networks (PDD DQN) as it starts with better scores on the first million steps on 41 of 42 games and on average it takes PDD DQN 83 million steps to catch up to DQfD's performance. DQfD learns to out-perform the best demonstration given in 14 of 42 games. In addition, DQfD leverages human demonstrations to achieve state-of-the-art results for 11 games. Finally, we show that DQfD performs better than three related algorithms for incorporating demonstration data into DQN.

Difference between training and test data distribution


I think you're confusing the underlying distribution from which both training and test distributions are drawn, with the distributions of the specific train and test draws. Unless the underlying distribution is eg time-sensitive, changed during the time between eg drawing the training and the testing samples, the underlying distribution is identical each time. The goal in learning a machine learning model is typically not to learn the training distribution, but to learn the latent underlying distribution, of which the training distribution is only a sample. Of course, you cannot actually see the underlying distribution, but eg, if you only really cared about learning the training samples, you could simply memorize the training samples in a lookup table, end of story. In reality, you are using the training sample as a proxy into the underlying distribution.

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AI in Corporate Learning: Actionable Guide on Employee Training [NEW]


Corporate learning is nothing new for most organizations, and many global players have already realized its advantages and have invested in improving their learning programs. Despite those growing efforts, many organizations are having a hard time convincing employees to utilize corporate learning. Organizations must realize that in the workplace, employees are their internal customers; employees buy in to the company mission and become advocates for the brand to attract more talent, and they are more engaged to gain more skills that will in turn make the company better. The feeling of the content being personalized is the key to learner engagement. The individual, their need for an individual learning experience, and their case-by-case provision of content pieces appear to be an impossible task at first, but AI is a great tool to help with this.