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...
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 steps. First, we systematically cover approaches to dynamics model learning, including ...
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...
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 ...
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...
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 ...
Survey of Model-Based Reinforcement Learning Applications on Robotics 基于模型的强化学习在机器人的应用 摘要 强化学习是机器人学习新任务的一种很有吸引力的方法。相关文献提供了大量的方法,但同时也清楚地表明了在现实环境中也有许多挑战。目前的预期提高了对适应性机器人的需求。我们认为,采用基于模型的强化学习,...
This survey focuses on deep learning solu... L Tai,J Zhang,M Liu,... 被引量: 20发表: 2016年 Design of a Control Architecture for Habit Learning in Robots Computational neuroscience models have formalized this as a coordination of model-based and model-free reinforcement learning. From this ...
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 ...
In the model-based reinforcement learning method, if the state transition model can capture the real environment, the agent can reach the next state only by interacting with the learned state transition model. Thus, it could significantly reduce the interaction between the agent and the real enviro...