一、Model - based（基于模型）- 可以把它想象成你在玩一个新游戏，手里有游戏的攻略。这个“攻略”...
Model-based and model-free learning strategies for wet clutch control[J] . Abhishek Dutta,Yu Zhong,Bruno Depraetere,Kevin Van Vaerenbergh,Clara Ionescu,Bart Wyns,Gregory Pinte,Ann Nowe,Jan Swevers,Robin De Keyser.Mechatronics . 2014A. Dutta, Y. Zhong, Depraetere B, et al, "Model-...
1. mf可以利用nn做function approximator，确实能做到近似最优policy或者q，但是nn本身需要大量样本收敛。
Model指的是针对环境的建模,即输入Action,环境的响应:Reward和State。 Model-Free:环境对输入的响应就是一个映射,without model,如常见的深度强化学习DQN/A3C/PPO等; Model-Based:环境对输入的响应是统计概率分布P(s_new|s,a)及P(r|s,a),如动态规划等传统强化学习方法。... ...
首先分析下主流的model-free方法非常流行的原因。model-free 的代码实现难度相对于model-based 的而言，...
In this task, the reward contingency is fixed for each state of the final choice, which enabled us to examine the change in the weight for model-based and model-free learning for an individual. The results showed that proselfs had a larger mean reward gain in the early phase of the ...
Learning Representation and Control in Markov Decision Processes: New Frontiers Model-based and model-free variants of the RPI algorithm are presented; they are also compared experimentally on discrete and continuous MDPs. Some ... Mahadevan,Sridhar - 《Foundations & Trends® in Machine Learning》...
文章要点:这篇文章提出了一个Model-Based Model-Free (MBMF)算法,通过学习一个dynamics model然后作为先验来做model free optimization,这里的model free optimization指的是基于Gaussian Process (GP) 的Bayesian Optimization (BO)。 具体的,如果dynamics model是未知的,就先学一个 ...
最早的Hybrid方法,即Dyna算法是由sutton在《Integrated architectures for learning, planning, and reacting based on approximating dynamic programming.》这篇文章中提出的。其算法思路结合了Model-free中的Q Learning和model-based中的model learning部分。
sample efficiency【Model-free -> Model-Based】 model-based算法就是专门解决sample-efficiency问题,通过先对环境建模,然后利用环境模型与agent进行交互,采集轨迹数据优化策略,整个建模流程中,planning步骤至关重要,正是通过在learned model基础上做planning才提高了整个强化学习算法迭代的效率,但是环境模型在学习过程中难免...