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Poster
Principled Exploration via Optimistic Bootstrapping and Backward Induction
Chenjia Bai · Lingxiao Wang · Lei Han · Jianye Hao · Animesh Garg · Peng Liu · Zhaoran Wang

Wed Jul 21 09:00 AM -- 11:00 AM (PDT) @
in Poster Session 3 »

One principled approach for provably efficient exploration is incorporating the upper confidence bound (UCB) into the value function as a bonus. However, UCB is specified to deal with linear and tabular settings and is incompatible with Deep Reinforcement Learning (DRL). In this paper, we propose a principled exploration method for DRL through Optimistic Bootstrapping and Backward Induction (OB2I). OB2I constructs a general-purpose UCB-bonus through non-parametric bootstrap in DRL. The UCB-bonus estimates the epistemic uncertainty of state-action pairs for optimistic exploration. We build theoretical connections between the proposed UCB-bonus and the LSVI-UCB in linear setting. We propagate future uncertainty in a time-consistent manner through episodic backward update, which exploits the theoretical advantage and empirically improves the sample-efficiency. Our experiments in MNIST maze and Atari suit suggest that OB2I outperforms several state-of-the-art exploration approaches.

Keywords: Reinforcement Learning and Planning

Author Information

Chenjia Bai (Harbin Institute of Technology)
Lingxiao Wang (Northwestern University)
Lei Han (Tencent AI Lab)
Jianye Hao (Tianjin University)
Animesh Garg (University of Toronto, Vector Institute, Nvidia)
Peng Liu (Harbin Institute of Technology)
Zhaoran Wang (Northwestern University)

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