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Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning

Luisa Zintgraf · Leo Feng · Cong Lu · Maximilian Igl · Kristian Hartikainen · Katja Hofmann · Shimon Whiteson

[ Abstract ] [ Livestream: Visit Multi-task Learning 1 ] [ Paper ]
Thu 22 Jul 5:40 p.m. — 5:45 p.m. PDT
[ Paper ]

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.

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