Double DQN 在Nature DQN中,计算Q现实时选择Q值是按照max的方式选择的,所以可能导致高估。 为了解决高估带来的影响我们可以引进另外一个神经网络。恰巧在DQN中存在两个参数不同结构相同的神经网络。因此可以先利用Qeval网络得到Q值,然后选择Qmax对应的action。然后再用Qnext网络得到最终的Q值。 DQN-Prioritized Experience...
reinforcement-learninggaedeep-reinforcement-learningrainbowpython3pytorchdeep-q-networkactor-criticdouble-dqnquantile-regressiondueling-dqncategorical-dqnppoadvantage-actor-critica2cdeep-recurrent-q-networkprioritized-experience-replaymulti-step-learningnoisy-networksdeeprl-tutorials ...
Deep Reinforcement Learning with Double Q-learning[arxiv][code] Dueling Network Architectures for Deep Reinforcement Learning[arxiv][code] Prioritized Experience Replay[arxiv][code] Noisy Networks for Exploration[arxiv][code] A Distributional Perspective on Reinforcement Learning[arxiv][code] ...
This study proposes a MASS autonomous navigation system using dueling deep Q networks prioritized replay (Dueling-DQNPR) based on the ship automatic identification system (AIS) big data. A navigation environment with three difficulty levels were established to train the Dueling-DQNPR network in ...
This paper proposes an Improved Dueling Deep Double-Q Network Based on Prioritized Experience Replay (IPD3QN) to address the slow and unstable convergence of traditional Deep Q Network (DQN) algorithms in autonomous path planning of USV. Firstly, we use the deep double Q-Network to decouple the...
Deep reinforcement learning with double q-learning. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA, 12–17 February 2016. [Google Scholar] Wang, Z.; Schaul, T.; Hessel, M.; Hasselt, H.; Lanctot, M.; Freitas, N. Dueling network architectures ...
In response to these challenges, this paper presents a novel algorithm called PER-n2D3QN, which integrates prioritized experience replay, a noisy network with factorized Gaussian noise, n-step learning, and a dueling structure into a double deep Q-network. This combination enhances the efficiency ...
For instance, double DQN [19], which is devoid of additional networks or parameters, can effectively address the problem e.g., the action-value function 𝑄(𝑠,𝑎)Q(s,a) is highly overestimated in Q-learning. Dueling DQN [20] decouples value and advantage in DQN through dueling ...