一、概述 在强化学习中,Policy Gradient(策略梯度)算法是一类通过优化策略函数直接来求解最优策略的方法。与基于值函数(例如Q学习和SARSA)的方法不同,策略梯度方法直接对策略函数进行建模,目标是通过梯度下降的方法来最大化预期的累积奖励(即期望回报)。这些算法主要适用于连续的动作空间或高维问题,能够在复杂的环境中...
策略梯度（Policy Gradient, PG）算法是强化学习中一类非常重要的算法，属于策略优化（Policy Optimization）...
Policy Gradient(策略梯度) 概念范围:Policy Gradient 是一类用于优化策略的算法,而不是一个具体的算法。 基础理论:Policy Gradient 方法基于梯度上升来优化一个目标函数(通常是期望回报)。 连续和离散动作空间:适用于连续和离散的动作空间。 算法多样性:包括多种算法,如 REINFORCE、PPO(Proximal Policy Optimization)、...
why it works, and many new policy gradient algorithms proposed in recent years: vanilla policy gradient, actor-critic, off-policy actor-critic, A3C, A2C, DPG, DDPG, D4PG, MADDPG, TRPO, PPO, ACER, ACTKR, SAC, TD3 & SVPG.
The policy gradient (PG) algorithm is a on-policy reinforcement learning method for environments with a discrete or continuous action space. A policy gradient agent uses the REINFORCE algorithm to directly estimate a stochastic policy. As REINFORCE belongs to the class of Monte Carlo methods, learni...
[1] Williams, Ronald J. “Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning.”Machine Learning8, no. 3–4 (May 1992): 229–56.https://doi.org/10.1007/BF00992696. [2] Sutton, Richard S., and Andrew G. Barto.Reinforcement Learning: An Introduction. Second...
深入解析策略梯度算法，从数学角度探究其核心原理与REINFORCE算法的具体推导。策略梯度算法与基于值函数优化的算法之间的显著区别在于，前者关注策略本身，而不是环境的状态价值函数，这使得策略梯度方法更直接地优化策略参数，以提升智能体在环境中的表现。在策略梯度的推导中，我们首先聚焦于REINFORCE算法，其...
DRL — Policy Based Methods — Chapter 3-3 Policy Gradient Methods,程序员大本营,技术文章内容聚合第一站。
Policy Gradient Methods (PG) are frequently used algorithms in reinforcement learning (RL). The principle is very simple. We observe and act. A human takes actions based on observations. As a quote from Stephen Curry: You have to rely on the fact that you put the work in to create the ...
· Marcello Restelli3 Received: 18 November 2021 / Revised: 13 May 2022 / Accepted: 9 August 2022 / Published online: 20 October 2022 © The Author(s) 2022 Abstract Policy gradient (PG) algorithms are among the best candidates for the much-anticipated applications of reinforcement learning...