Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs

Understand

Reinforcement Learning (RL) algorithms typically require millions of environment interactions to learn successful policies in sparse reward settings.

• Hindsight Experience Replay (HER) was introduced as a technique to increase sample efficiency by reimagining unsuccessful trajectories as successful ones by altering the originally intended goals.
• However, it cannot be directly applied to visual environments where goal states are often characterized by the presence of distinct visual features.
• In this work, we show how visual trajectories can be hallucinated to appear successful by altering agent observations using a generative model trained on relatively few snapshots of the goal.