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HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning
Ziniu Li · Yingru Li · Yushun Zhang · Tong Zhang · Zhiquan Luo

Tue Dec 14 09:00 AM -- 10:00 AM (PST) @
in Ecological Theory of Reinforcement Learning: How Does Task Design Influence Agent Learning? »
Randomized least-square value iteration (RLSVI) is a provably efficient exploration method. However, it is limited to the case where 1) a good feature is known in advance and 2) this feature is fixed during the training: if otherwise, RLSVI suffers an unbearable computational burden to obtain the posterior samples of the parameter in the $Q$-value function. In this work, we present a practical algorithm named HyperDQN, addressing these two issues under the context of deep reinforcement learning, where the feature changes over iterations. HyperDQN is built on two parametric models: in addition to a non-linear neural network (i.e., base model) that predicts $Q$-values, our method employs a probabilistic hypermodel (i.e., meta model), which outputs the parameter of the base model. When both models are jointly optimized under a specifically designed objective, three purposes can be achieved. First, the hypermodel can generate approximate posterior samples regarding the parameter of the $Q$-value function. As a result, diverse $Q$-value functions are sampled to select exploratory action sequences. This retains the punchline of RLSVI for efficient exploration. Second, a good feature is learned to approximate $Q$-value functions. This addresses limitation 1. Third, the posterior samples of the $Q$-value function can be obtained in a more efficient way than the existing methods, and the changing feature does not affect the efficiency. This deals with limitation 2. On the Atari 2600 suite, after $20$M samples, HyperDQN achieves about $2 \times$ improvements over (double) DQN, the advanced method Bootstrapped DQN, and the SOTA exploration bonus method OB2I. For another challenging task SuperMarioBros, HyperDQN outperforms baselines on $7$ out of $9$ games.

Author Information

Ziniu Li (The Chinese University of Hong Kong, Shenzhen)
Yingru Li (The Chinese University of Hong Kong, Shenzhen)
Yushun Zhang (The Chinese University of Hong Kong, Shenzhen)

I am a Ph.D. student under the supervision of Prof. Tom Zhi-Quan Luo and Prof. Tong Zhang, I am interested in understanding deep learning.

Tong Zhang (Tencent AI Lab)
Zhiquan Luo (The Chinese University of Hong Kong, Shenzhen and Shenzhen Research Institute of Big Data)

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