Two key approaches to this problem are reinforcement learning (RL) and planning. This survey is an integration of both fields, better known as model-based reinforcement learning. Model-based RL has two main step
最近在看model-based RL, 本文也是基于综述文章的理解:Model-based Reinforcement Learning: A Survey 此外,推荐另外一篇benmark的文章:Benchmarking Model-Based Reinforcement Learning 基于模型的强化学习(Model-based RL),顾名思义,分为两个部分,模型和决策。如果模型已知,那么只需要考虑如何根据模型进行决策,如果模...
小结:most model-based RL methods focus on:a forward model, with function approximation, and global coverage. 【Stochasticity——aleatoric uncertainty任意不确定性,never be reduced】 Descriptive models、Generative models High-dimensional:based on neural network (Deep generative models) 【Uncertainly——epist...
题目:Model-based Reinforcement Learning: A Survey 引言: 近年来,在强化学习的研究中,模型为基础的强化学习(model-based reinforcementlearning)方法越来越受到关注。相比于传统的基于价值函数或策略函数的模型无关的强化学习方法,模型为基础的强化学习方法利用环境模型来显式地估计环境的动态特性,从而更高效地学习到最优...
Survey of model-based reinforcement learning: Applications on robotics," Journal of Intelligent & Robotic Systems, vol. 86, no. 2, pp. 153-173, 2017.A. S. Polydoros and L. Nalpantidis, "Survey of model-based reinforcement learning: Applications on robotics," J. Intell. Robot. Syst., ...
最后Implicit Model-based Reinforcement Learning这部分,提出了一个隐式学习的观点,比如整个问题都可以看做是model free方法,里面的各个模块只是来解决这个问题的隐式方法,我们并不需要作区分(In other words, the entire model based RL procedure (model learning, planning, and possibly integration in value/policy...
Model-Based Reinforcement Learning: A Survey Introduction: Model-based reinforcement learning (MBRL) is a popular approach in the field of reinforcement learning (RL) that utilizes a learned model of theenvironment to optimize an agent's behavior. In this survey, we will delve into the key conc...
based reinforcement learn- ing, the—now limited—adaptability characteristics of robotic systems can be expanded. Also, model-based reinforcement learning exhibits advantages that makes it more applicable to real life use-cases compared to model-free methods. Thus, in this survey, model- based ...
Model-based reinforcement learning for robot control offers the advantages of overcoming concerns on data collection and iterative processes for policy improvement in model-free methods. However, both methods use exploration strategy relying on heuristics that involve inherent randomness, which may cause in...
Also, model-based reinforcement learning exhibits advantages that makes it more applicable to real life use-cases compared to model-free methods. Thus, in this survey, model-based methods that have been applied in robotics are covered. We categorize them based on the derivation of an optimal ...