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To rapidly learn a new task, it is often essential for agents to exploreefficiently -- especially when performance matters from the first timestep. Oneway to learn such behaviour is via meta-learning. Many existing methods howeverrely on dense rewards for meta-training, and can fail catastrophically if therewards are sparse. Without a suitable reward signal, the need for explorationduring meta-training is exacerbated. To address this, we propose HyperX, whichuses novel reward bonuses for meta-training to explore in approximatehyper-state space (where hyper-states represent the environment state and theagent's task belief). We show empirically that HyperX meta-learns bettertask-exploration and adapts more successfully to new tasks than existingmethods.

Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning