Soft Q-Learning是最近出现的一组最大熵(maximum entropy)框架的无模型深度学习中的代表作。事实上,最大熵强化学习在过去十几年间一直都有在研究,但是最近又火了起来,这和Soft Q-Learning以及后续的Soft Actor-Critic诞生密切相关。 背景介绍 对于无模型强化学习算法,我们从探索(exploration)的角度考虑。尽管随机策略...
论文《Inverse Factorized Soft Q-Learning for Cooperative Multi-agent Imitation Learning》来自 NeurIPS 2024。这篇论文研究多智能体环境中的模仿学习问题,提出 Multi-agent Inverse Factorized Q-learning (…
Reinforcement Learning with Deep Energy-Based Policies# 论文地址# soft Q-learning 笔记# 标准的强化学习策略 π∗std=argmaxπ∑tE(St,At)∼ρπ[r(St,At)](1)(1)πstd∗=argmaxπ∑tE(St,At)∼ρπ[r(St,At)] 最大熵的强化学习策略 π∗MaxEnt=argmaxπ∑tE(St,At)∼ρπ[r(St...
首先,要知道soft-learning是一个很老的算法,其实就是在q-learning的基础上加了个soft变换,然后在探索阶段不使用epsilon-greedy探索,而是使用soft-q作为探索方法,而在训练参数时候使用的update方法依然是q-learning的TD方法; 然后,要知道本文的soft q-learning与之前的传统的soft q-learning的不同,就像刚提到的,之前...
Soft Actor Critic 一共有3篇论文。单纯从方法上来看三篇论文是递进关系。第一篇:《Reinforcement Learning with Deep Energy-Based Policies》 这一篇是后面两篇论文的理论基础,推导了基于能量模型(加入熵函数)的强化学习基本公式,并且给出了一个叫做 Soft Q Learning的算法。但是策略网络需要使用SVGD方法优化,十分...
强化学习SQL算法(soft q learning)—— SVGD的实现(Stein Variational Gradient Desc,代码实现地址:https://openi.pcl.ac.cn/devilmaycry812839668/softlearning/src/branch/mast高维度复杂分布的近似问题。f
Soft Q-learning can be run either locally or through Docker.PrerequisitesYou will need to have Docker and Docker Compose installed unless you want to run the environment locally.Most of the models require a MuJoCo license.Docker Installation
前人对熵强化学习的研究集中在off-policy 的 Q-learning。首先,我觉得现有的理论证明有点冗长,不够简洁,所以另辟蹊径,从另一个角度 —— Policy Gradient Theorem,来思考熵强化学习的问题。其次,我觉得业界低估了策略熵对exploration-exploitation平衡的统领作用,所以致力于推进熵强化学习,推出熵强化学习算法。最后,我...
illustrating our method can also be used for inverse reinforcement learning (IRL). Our method, Inverse soft-Q learning (IQ-Learn) obtains state-of-the-art results in offline and online imitation learning settings, significantly outperforming existing methods both in the number of required environment...
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