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A Dueling Network is a type of Q-Network that has two streams to separately estimate (scalar) state-value and the advantages for each action. Both streams share a common convolutional feature learning module. The two streams are combined via a special aggregating layer to produce an estimate of the state-action value function Q as shown in the figure to the right.
知乎专栏是一个自由表达和随心写作的平台，介绍了Dueling network论文及其网络架构。
Our dueling network represents two separate estimators: one for the state value function and one for the state-dependent action advantage function. The main benefit of this factoring is to generalize learning across actions without imposing any change to the underlying reinforcement learning algorithm. Our results show that this architecture ...
Our dueling network represents two separate estimators: one for the state value function and one for the state-dependent action advantage function. The main benefit of this factoring is to generalize learning across actions without imposing any change to the underlying reinforcement learning algorithm. Our results show that this architecture ...
Figure 1. This dueling network should be understood as a single Qnetwork with two streams that replaces the popu-lar single-stream Qnetwork in existing algorithms such as Deep Q-Networks (DQN; Mnih et al., 2015). The dueling network automatically produces separate estimates of the state value function and advantage function, without any extra ...
概要. Open AI GymのCartPoleで、Dueling NetworkにしたDQNの実装・解説をします。. プログラムが1ファイルで完結し、学習・理解しやすいようにしています。. 【対象者】. ・強化学習DQNの発展版に興味がある方. ・ 速習 強化学習: 基礎理論とアルゴリズム（書籍） を ...
