Dueling DDQN,即对决双Q学习可以是在原始的DQN上进行的改进,具体可以参考这本书(王树森的《深度强化学习》)的教程,非常详细。 cartpole 在之前gymnasium安装时已经一并安装好了(参考之前的笔记),flappy bird安装参考官网教程: https://github.com/markub3327/flappy-bird-gymnasium 实际上,只需要两行代码: #首先使用...
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)...
N-step-Dueling-DDQN-PER-Pacman Using N-step dueling DDQN with PER for learning how to play a Pacman game SummaryDeepMind 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...
reinforcement-learningdeep-learningpython3datasetpytorch-implementationdueling-ddqndueling-dqn-pytorchirl-algorithmsgail-ppo UpdatedApr 5, 2022 Python This project uses Deep Reinforcement Learning to solve the Lunar Lander environment of the OpenAI-Gym ...
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. ...
In this paper, Dueling Network Architectures for Deep Reinforcement Learning we use the improved Double DQN (DDQN) learning al- function. As in (Mnih et al., 2015), the output of the net- gorithm of van Hasselt et al. (2015). In Q-learning and work is a set of values, one for ...
For example, DDQN [6], a target network is added on the basis of DQN, which can reduce overestimation to some extent. D3QN uses Dueling Network [7] architecture on the basis of DDQN. It uses the network to express two estimators, namely the state value function and the action advantage ...
For example, DDQN [6], a target network is added on the basis of DQN, which can reduce overestimation to some extent. D3QN uses Dueling Network [7] architecture on the basis of DDQN. It uses the network to express two estimators, namely the state value function and the action advantage ...
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。所以在DDQN里面,我们会用会更新参数的Q网络去选动作,用目标Q网络(固定住的网络)计算值。 改进 论文出处 dueling DQN 相较于原来的 深度Q网络,它唯一的差别是改变了网络的架构。 原来的深度Q网络直接输出 Q 值,竞争深度Q网络不直接输出 Q 值,而是...
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 ...