Value-Based Reinforcement Learning 一、Deep Q-Network (DQN) 本质就是用神经网络近似Q∗Q∗函数,将Q∗(st,at)Q∗(st,at)当作是一个先知,先知可以告诉你每个动作带来的平均回报,我们就应该听先知的话选平均回报最高的动作 Goal: Win the game (≈ maximize the total reward.) ...
这是对TD算法的改进。前面注意到无论是Sarsa还是 Q-Learning, TD target中都只包含了一个真实的奖励r_t,而Multi-Step TD Target是包含多个奖励r_t,r_{t+1} ..., 两步可以写成: U_t=R_t+\gamma\cdot R_{t+1}+\gamma^2\cdot U_{t+2}. m步的Sarsa可以写成: y_t=\sum_{i=0}^{{m-1}}...
当然,基于值的强化学习方法还有许多改进的方法以及一些新的研究思路,包括多步学习、噪声网络和值分布强化学习,具体可以参考《Deep Reinforcement Learning》的4.7节。 参考 《Deep Reinforcement Learning Book》中第4章 强化学习路线 值分布强化学习发展 值分布强化学习C51详解 dqn简单实现和训练流程 tianshou中PER的详细实...
Reinforcement Learning(二):Value-Based 回顾一下action-value函数: Value-Based是指: 但是一般来说,这个Q*我们是无从得出的,因此提出使用卷积网络来近似: Deep Q-Network (DQN) Approximate the Q Function Deep Q Network (DQN) Apply DQN to Play Game Temporal Difference (TD) Learning 一个小例... ...
23 p. Enhancing Motion in Text-to-Video Generation with Decomposed Encoding and Conditioning 18 p. Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language Use 5 p. Revisiting Joule-expansion experiments with a quantum gas 关于...
Tabular Value-Based Reinforcement Learning: An Introduction and Step-by-Step Guide Introduction: Reinforcement learning (RL) is a branch of machine learning that focuses on training agents to makesequential decisions in order to maximize a cumulative reward. Value-based RL is one popular approach wit...
Reinforcement learning (RL) is developed to address the problem of how to make a sequential decision. The goal of the RL algorithm is to maximize the total reward when the agent interact with the environment. RL is very successful in many traditional fields for decades. From another aspect of...
本文介绍Youtube在2019年放出的两篇强化学习推荐系统中基于value-based的一篇,论文标题:SLATEQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets (IJCAI 2019) 原文地址: https://arxiv.org/pdf/1905.12767.pdf ...
reinforcement learningvalue-basedRealising adaptive traffic signal control (ATSC) through reinforcement learning (RL) is an important means to easetraffic congestion. This paper finds the computing power of the central processing unit (CPU) cannot fully usedwhen Simulation of Urban MObility (SUMO) is ...
Deep Reinforcement Learning (DRL) has been increasingly attempted in assisting clinicians for real-time treatment of sepsis. While a value function quantifies the performance of policies in such decision-making processes, most value-based DRL algorithms