Deep Q-Learning Algorithm 在具体介绍 Deep Q-Learning 算法前,我们先来快速回顾一下基于 tabular method 的传统 Q-Learning 算法。在 Q-Learning 中,每个 Q-value 的更新逻辑如下正如在上一篇文章中介绍的那样,其本质上是通过 TD Learning 的思想构造 【TD Target】,然后与当前的
N. Sprague. Parameter Selection for the Deep Q-learning Algorithm ((Extended Abstract)). In Proceedings of the Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2015.N. Sprague, "Parameter Selection for the Deep Q-learning Algorithm ((Extended Abstract))," in ...
使用DQN 网络计算当前状态下采取当前动作的Q值[Q ( S t , A t ) Q(S_t, A_t)Q(St,At)]。 使用目标网络计算在下一状态下能得到的最大目标 Q 值以及奖励[R T + 1 + γ m a x Q ( S t + 1 , a ) R_{T+1}+\gamma max Q(S_{t+1}, a)RT+1+γmaxQ(St+1,a)...
Q-learning 是强化学习中一种经典的算法,它通过学习状态 - 行动对(State-Action Pair)的 Q 值来指导智能体的行为。然而,传统的 Q-learning 算法在面对状态空间巨大的场景时(如游戏、机器人控制等)存在明显的局限性,因为直接存储和更新所有状态 - 行动对的 Q 值在计算和存储上是不可行的。 深度Q 网络(Deep ...
The Deep Q-Learning Algorithm Deep Q-Learning使用深度神经网络来近似每个状态下不同动作的 Q 值(价值函数估计)。与传统的 Q 学习不同之处在于,在训练阶段,它使用梯度下降来更新深度 Q 网络的权重,以更好地逼近我们的 Q 值预测与 Q 目标之间的差异。
双重Deep Q-Learning,处理Q值过高的问题(Double DQN) Deep Q-Learning training algorithm Experience Replay to make more efficient use of experiences: 经验回放(Experience Replay) 在Deep Q-Learning 中有两个功能: 在培训期间更有效地利用经验 通常,在线强化学习中,代理在环境中交互,获取体验(状态、行动...
Double Deep Q-Learning:用于解决Q值过高估计的问题。 3.1 Experience Replay(经验回放) 如图所示,Experience Replay组件采用ε-greedy策略与环境进行交互(当前状态下采取可能得到最高收益的动作),并得到环境反馈的奖励和下一状态,并将这一观察结果保存为训练数据样本(Current State, Action, Reward, Next State)。训练...
https://www.analyticsvidhya.com/blog/2019/01/monte-carlo-tree-search-introduction-algorithm-deepmind-alphago/ 强化学习的基础:时间差(TD)学习介绍 https://www.analyticsvidhya.com/blog/2019/03/reinforcement-learning-temporal-difference-learning/?utm_source=blog&utm_medium=introduction-deep-q-learning-pyth...
https://www.analyticsvidhya.com/blog/2019/01/monte-carlo-tree-search-introduction-algorithm-deepmind-alphago/ 强化学习的基础:时间差(TD)学习介绍 https://www.analyticsvidhya.com/blog/2019/03/reinforcement-learning-temporal-difference-learning/?utm_source=blog&utm_medium=introduction-deep-q-learning-pyth...
An online deep Q-learning algorithm has been presented to deliver the best policy on the fly. To expedite learning, the PDS method employs partially known knowledge about dynamic systems and allows the edge node to incorporate such knowledge into its learning experience. The DO2QIEO method is ...