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Model-free reinforcement learning (RL) requires a large number of trials tolearn a good policy, especially in environments with sparse rewards. We explorea method to improve the sample efficiency when we have access todemonstrations. Our approach, Backplay, uses a single demonstration toconstruct a curriculum for a given task. Rather than starting each trainingepisode in the environment's fixed initial state, we start the agent near theend of the demonstration and move the starting point backwards during thecourse of training until we reach the initial state. Our contributions are thatwe analytically characterize the types of environments where Backplay canimprove training speed, demonstrate the effectiveness of Backplay both in largegrid worlds and a complex four player zero-sum game (Pommerman), and show thatBackplay compares favorably to other competitive methods known to improvesample efficiency. This includes reward shaping, behavioral cloning, andreverse curriculum generation.

Backplay: "Man muss immer umkehren"