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...
Soft Q-learning这篇论文证明energy-based policy是maximum-entropy强化目标函数的最优解: 既然energy-based policy取决于Q函数,那么最大的问题就是怎么求Q?这个Q值和经典Q-learning的Q值定义不一样哦,它含有entropy一项。作者模仿Bellman equation设计了一个soft Bellman equation: 其中, 作者证明了:只要对soft Bellman...
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...
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...
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 interactions and scalability in high-dimensional spaces, often by more than 3x. ...
Current frameworks for stochastic games and reinforcement learning prohibit tuneable strategies as they seek optimal performance. In this paper, we enable such tuneable behaviour by generalising soft Q-learning to stochastic games, where more than one agent interact strategically. We contribute both ...
docker exec -it soft-q-learning bash See examples section for examples of how to train and simulate the agents. To clean up the setup: docker-compose down Local Installation To get the environment installed correctly, you will first need to clonerllab, and have its path added to your PYTHON...
Please update the paths etc based on your own usages. sql/ This directory contains the core components of the soft Q-learning algorithm for text generation. modules/ This directory contains the core components of the models and GEM-metrics....
Double Q-Learning(DQL)的出现解决了过估计问题,但同时造成了低估问题。为解决以上算法的高低估问题,提出了基于softmax的加权Q-Learning算法,并将其与DQL相结合,提出了一种新的基于softmax的加权Double Q-Learning算法(WDQL-Softmax)。该算法基于加权双估计器的构造,对样本期望值进行softmax操作得到权重,使用权重...
;Q-learning单步更新critic学习奖惩机制,环境和奖惩之间的关系可以使actor单步更新 problem:连续学习连续更新,前后存在相关性 solve:actor-critic...中) 根据最高价值选择动作 用概率分布在连续的动作中选择特定的动作 ×policygradients Q-learning、SarsaActor-Critic是两者的结合。actor ...