Learning Montezuma's Revenge from a Single Demonstration

Understand

We propose a new method for learning from a single demonstration to solve hard exploration tasks like the Atari game Montezuma's Revenge.

• Instead of imitating human demonstrations, as proposed in other recent works, our approach is to maximize rewards directly.
• Our agent is trained using off-the-shelf reinforcement learning, but starts every episode by resetting to a state from a demonstration.
• By starting from such demonstration states, the agent requires much less exploration to learn a game compared to when it starts from the beginning of the game at every episode.