  To rapidly learn a new task, it is often essential for agents to explore
efficiently -- especially when performance matters from the first timestep. One
way to learn such behaviour is via meta-learning. Many existing methods however
rely on dense rewards for meta-training, and can fail catastrophically if the
rewards are sparse. Without a suitable reward signal, the need for exploration
during meta-training is exacerbated. To address this, we propose HyperX, which
uses novel reward bonuses for meta-training to explore in approximate
hyper-state space (where hyper-states represent the environment state and the
agent's task belief). We show empirically that HyperX meta-learns better
task-exploration and adapts more successfully to new tasks than existing
methods.
