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-...
在深度强化学习（Deep Reinforcement Learning, DRL）中，Model-based（基于模型）和Model-free（无模型）...
Computational analyses of instrumental learning (involved in predicting which actions will be rewarded) have paid substantial attention to the critical distinction betweenmodel-freeandmodel-basedforms of learning and computation (see Fig.1). Model-based strategies generate goal-directed choices employing a...
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。... ...
一、Model-Free vs Model-Based 从这一章开始,我们进入这个系列教程对RL的分类体系中的第三个类别:基于模型的强化学习(Model-Based Reinforcement Learning, MBRL)。 与之相对地,我们之前介绍的那些方法,可以称作无模型强化学习(Model-Free RL),因为它们直接学习策略函数或者价值函数,并没有对环境进行建模。也就是...
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 ...
we propose parallel reinforcement-learning models of card sorting performance, which assume that card sorting performance can be conceptualized as resulting from model-free reinforcement learning at the level of responses that occurs in parallel with model-based reinforcement learning at the categorical lev...
Computationally, model-based and model-free learning contain terms that are in some cases shared and in other cases unique to themselves. Pre-eminent in model-free learning is the reward prediction error (RPE), the difference between expected and obtained reward, which is used to adjust action ...
Reinforcement Learning:Policy Gradient Actor-Critic Policy Gradient Introduction 上一节说的是value function approximation,使用的是函数拟合。这一节说的就是采用概率的方法来表示:这一节主要是讲model-free的方法。 RL有value-base,policy-based,以及把两者进行结合的actor-aritic的方法。 使用policy-based RL的...
To address the difficulty of designing a controller for complex visual-servoing tasks, two learning-based uncalibrated approaches are introduced. The first method starts by building an estimated model for the visual-motor forward kinematic of the vision-robot system by a locally linear regre...