A: 在DDQN里面,选动作的Q函数与计算值的Q函数不是同一个。在 DDQN 里面有两个 Q网络,第一个 Q网络 Q 决定哪一个动作的 Q 值最大(我们把所有的a代入 Q 函数中,看看哪一个a的Q 值最大)。我们决定动作以后,Q 值是用Q′ 算出来的。 我们动手实现的时候,有两个Q网络:会更新的Q网络和目标Q网络。所以...
而DDQN不同,DDQN首先从Q网络中找到最大输出值的那个动作,然后再找到这个动作对应的Target Q网络的输出值。这么做的原因是传统的DQN通常会高估Q值得大小,两者代码差别如下: q_eval=self.eval_net(batch_state).gather(1,batch_action)q_next=self.target_net(batch_next_state).detach()ifself.double:#ddqnq_n...
reinforcement-learningopenai-gympytorchdqnddpgddqnppotd3dueling-ddqn UpdatedOct 30, 2020 Jupyter Notebook Various Deep RL models applied to Super Mario Bros deep-reinforcement-learningdqnddqndeep-q-learningmario-brosdueling-ddqn UpdatedMar 1, 2022 ...
Using N-step dueling DDQN with PER for learning how to play a Pacman game Summary DeepMind published its famous paper Playing Atari with Deep Reinforcement Learning, in which a new algorithm called DQN was implemented. It showed that an AI agent could learn to play games by simply watching ...
DDQN Dueling DDQN Both can be enhanced withNoisy layer,Per(Prioritized Experience Replay),Multistep Targetsand be trained in aCategorical version (C51). Combining all these add-ons will lead to thestate-of-the-artAlgorithm of value-based methods called:Rainbow. ...
本文从网络结构上入手,对现有的算法包括DQN、Double DQN以及PER算法进行了改进。 2. 算法原理和过程 文中第一章就直接向我们展示了提出的“dueling architecture”结构,如图所示: 图中将原有的DQN算法的网络输出分成了两部分:即值函数和优势函数共同组成,在数学上表示为: Q(s, a ; \theta, \alpha, \beta)...
Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL) machine-learningreinforcement-learningasldeep-reinforcement-learningq-learningpytorchddpgsacdouble-dqnc51dueling-dqncategoric...
值得一提的是,我们在训练Dueling Network时,可以采用的训练方法:PER,DDQN,Multi-Step TD target等 三、解决不唯一性(Overcome Non-identifiability) 如果不加最大化,可能会出现V和A都训练不好。举个例子,如果记V' = V*+10,A' = A*-10,它们的和并不会对Q产生影响,但实际上,V*和A*都产生了很大的波动,...
- [Double DQN (DDQN)](https://arxiv.org/pdf/1509.06461.pdf) - [Double DQN](https://arxiv.org/pdf/1509.06461.pdf) - [Dueling DQN](https://arxiv.org/pdf/1511.06581.pdf) - [Advantage Actor-Critic (A2C)](https://openai.com/blog/baselines-acktr-a2c/) - [Deep Deterministic Policy Gra...
self.target_dqn = DDQN.ddqn(num_action) self.render_image = False self.frame_counter = 0. # Counts the number of steps so far self.annealing_count = 0. # Counts the number of annealing steps self.epis_count = 0. # Counts the number episodes so far ...