At its core, Q-learning involves learning an optimal policy through trial and error, aiming to maximize cumulative rewards by making sequential decisions. The algorithm learns a value function, often called Q-values, representing the expected long-term rewards of actions in specific states. This mo...
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Q-Learning是一个基于值的增强学习算法。内容如下: What Q-Learning is How to implement it with Numpy Q-table是如果在特定状态下,选择最优策略的情况下,未来最大的的期待奖励的值。 为了学习Q-table,要用到Q Learning算法。 Q-learning:Action-Value Function(也叫作Q function)输入是state和action。这个函...
The Q-function then generates outputs along with expected future rewards for that action in the specific state. The Q-table allows the agent to look up the expected future reward for any given state-action pair to move toward an optimized state. What is the Q-learning algorithm process? The...
2.1、函数近似(Function Approximation) 在此之前介绍的强化学习方法(动态规划、蒙特卡罗、时序差分)都有一个共同前提:状态空间和动作空间是离散的且不能太大。通常值函数用表格的形式的表示,故又称之为表格型强化学习。而在很多问题中,状态空间维数很大,或者状态空间是连续的,无法用表格表示,故需要函数近似的方式。
BY571 / Normalized-Advantage-Function-NAF- Star 28 Code Issues Pull requests PyTorch implementation of the Q-Learning Algorithm Normalized Advantage Function for continuous control problems + PER and N-step Method reinforcement-learning q-learning dqn reinforcement-learning-algorithms continuous-cont...
Q -learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method f... CJCH Watkins,P Dayan - 《Machine Learning》 被引量: 2986发表: 1992年 ...
In this paper, we analyze the convergence properties of Q-learning using linear function approximation. This algorithm can be seen as an extension to stochastic control settings of TD-learning using linear function approximation, as described in [1]. We derive a set of conditions that implies the...
Reinforcement learningUtilitarianismEthicsHuman–machine teamingThis paper demonstrates that Q-learning can be used to model Utilitarian decision-making. Accurately modeling ethical theories from the field of moral philosophy is an important step in the development of ethical machine learning. Modeling ...
Q-Learning算法 Q-Learning算法中的“Q”代表着策略π的质量函数(Quality function),该函数能在观察状态s确定动作a后,把每个状态动作对 (s, a) 与总期望的折扣未来奖励进行映射。 Q-Learning算法属于model-free型,这意味着它不会对MDP动态知识进行建模,而是直接估计每个状态下每个动作的Q值。然后,通过在每个状态下...