Reward as Observation: Learning Reward-Based Policies for Rapid Adaptation
The paper proposes a reward-based policy that learns directly from rewards and actions, ignoring original observation spaces. This allows for zero-shot transfer to new environments with different observations. The method uses temporal history via LSTMs and expert guidance to achieve high performance across diverse tasks like Pointmass and Car Racing.