Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning

• URL: http://arxiv.org/abs/2010.01062v3
• Date: Wed, 9 Jun 2021 21:43:46 GMT
• Title: Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
• Authors: Luisa Zintgraf, Leo Feng, Cong Lu, Maximilian Igl, Kristian Hartikainen, Katja Hofmann, Shimon Whiteson
• Abstract summary: We propose HyperX, which uses novel reward bonuses for meta-training to explore in approximate hyper-state space. We show empirically that HyperX meta-learns better task-exploration and adapts more successfully to new tasks than existing methods.
• Score: 60.1292055717823
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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