In machine learning, Q-learning is a foundational reinforcement learning technique for decision-making in uncertain environments. Unlikesupervised learning, where models learn from labelled data, andunsupervised learning, where patterns are derived from unlabeled data, Q-learning operates within a framework...
可以看到，Q-learning寻找到一条全局最优的路径，因为虽然Q-learning的行为策略（behavior）是基于 ε-gr...
Reinforcement learning is training paradigm for agents in which we have example of problems but we do not have the immediate exact answer. For playing a game, for instance, an agent will make series of decisions to move and only later will find out whether those decisions are right or wrong...
Q-learning is a machine learning approach that enables a model to iteratively learn and improve over time by taking the correct action. Q-learning is a type of reinforcement learning. With reinforcement learning, a machine learning model is trained to mimic the way animals or children learn. Go...
Example code for deep Q-learning. Learn more about deep learning, machine learning MATLAB, Deep Learning Toolbox
load_in_8bit=True, ) model = AutoModelForCausalLM.from_pretrained(some-model-id, quantization_config=bnb_config) 由于BnB量化不需要任何校准数据集,因此其量化速度很快,这也是为什么在QLoRA训练时,会直接传入BitsAndBytesConfig直接对原始模型量化后训练。
Reinforcement learning (RL) is a machine learning technique aiming to learn how to take actions in an environment to maximize some kind of reward. Recent research has shown that although the learning efficiency of RL can be improved with expert demonstration, it usually takes considerable efforts ...
提到Q-learning，我们需要先了解Q的含义。Q为动作效用函数（action-utility function），用于评价在特定...
(RL) is a branch of machine learning that tackles problems where there’s no explicit training data with known, correct output values. Q-learning is an algorithm that can be used to solve some types of RL problems. In this article, I explain how Q-learning works and provide an example ...
Q-Learning 是最著名的强化学习算法之一。我们将在本文中讨论该算法的一个重要部分:探索策略。但是在开始具体讨论之前,让我们从一些入门概念开始吧。 强化学习(RL) 强化学习是机器学习的一个重要领域,其中智能体通过对状态的感知、对行动的选择以及接受奖励和环境相连接。在每一步,智能体都要观察状态、选择并执行一...