Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning

By Kibeom Kim, Min Whoo Lee, Yoonsung Kim, Je-hwan Ryu, Minsu Lee, Byoung-Tak Zhang. 2021

11 citations recorded by the source corpus.

Learning in a multi-target environment without prior knowledge about the targets requires a large amount of samples and makes generalization difficult. To solve this problem, it is important to be able to discriminate targets through semantic understanding. In this paper, we propose goal-aware cross-entropy (GACE) loss, that can be utilized in a self-supervised way using auto-labeled goal states alongside reinforcement learning. Based on the loss, we then devise goal-discriminative attention networks (GDAN) which utilize the goal-relevant information to focus on the given instruction. We evaluate the proposed methods on visual navigation and robot arm manipulation tasks with multi-target environments and show that GDAN outperforms the state-of-the-art methods in terms of task success ratio, sample efficiency, and generalization. Additionally, qualitative analyses demonstrate that our proposed method can help the agent become aware of and focus on the given instruction clearly, promoting goal-directed behavior.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Towards Continual Reinforcement Learning: A Review and Perspectives (2020, background): “…this phased learning setting can be adopted for continual learning deployment at meta-test time by training the inner loop over a sequence of tasks (Kim et al., 2021a; Javed and White, 2019; Spigler, 2019; Beaulieu, Frati, Miconi, Lehman, Stanley, Clune, and Cheney, 2020; Caccia, Rodriguez,…”