Deep Q-learning from Demonstrations

Understand

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.