Policy evaluationImportance samplingIn reinforcement learning, importance sampling is a widely used method for evaluating an expectation under the distribution of data of one policy when the data has in fact been generated by a different policy. Importance sampling requires computing the likelihood ratio...
对于在如下的环境中,小人要到达目标点,需要满足三个启发式的条件,1)看向终点2)避免进入威胁区3)避免电量用完,如果用传统的奖励函数设计的方法,在1)和2)两个约束下: 选定不同的1)约束的权重与2)约束的权重,可以看到图三在学习1)约束的时候都比较好,权重比较小的时候有一格比较差。2)约束就学习的稍差,综合...
Finally, this learning method was adopted to realize the self-adaptation action fusion of mobile robots in the task of obstacle avoidance. And its efficiency was validated by simulation results. 展开 关键词: behavior decomposition Reinforcement learning Q-learning obstacle avoidance ...
We show that a rich set of behavior variations can be captured by determining the appropriate reward function in the reinforcement learning framework, and show that the discovered reward function can be applied to different environments and scenarios. We also introduce a new algorithm to recover the...
A policy-blending formalism for shared control inverse reinforcement learning, discuss simplifying assumptions that make it tractable, and test these on data from users teleoperating a robotic manipulator. ... AD Dragan,SS Srinivasa - 《International Journal of Robotics Research》 被引量: 117发表: ...
The FNN is trained through reinforcement learning. At last, a velocity tuning module is designed to adjust the speed of the robot. Simulation results validate the feasibility of this method 展开 关键词: Practical, Theoretical or Mathematical/ collision avoidance fuzzy control fuzzy neural nets ...
Reinforcement learning is very useful for robots with little a priori knowledge in acquiring appropriate behavior. This paper describes a learning system which can learn a state representation and a behavior policy simultaneously while executing the task. We call the system - the situation transition ...
ABM-KEEP DOING WHAT WORKED: BEHAVIOR MODELLING PRIORS FOR OFFLINE REINFORCEMENT LEARNING Motivation offline场景下存在policy-shift问题,但是希望的是充分利用offline-data中提供信息的同时避免对状态-动作价值函数的错误的高估 本文的方法是stay close to the relevant data:对offline data学习一个先验分布,然后使得...
In the field of multiagent systems, some methods use the policy gradient method for behavior learning. In these methods, the learning problem in the multiagent system is reduced to each agent's independent learning problem by adopting an autonomous distributed behavior determination method. That is...
Direct policy search is one of the most important algorithm of reinforcement learning. However, learning from scratch needs a large amount of experience data and can be easily prone to poor local optima. In addition to that, a...