scholarly journals Timing and Parameter Optimization for One-time Motion Problem Based on Reinforcement Learning

2020 ◽  
Vol 5 (1) ◽  
pp. 10
Author(s):  
Boxuan Fan ◽  
Guiming Chen ◽  
Hongtao Lin
Keyword(s):  
Reinforcement Learning ◽  
Parameter Optimization ◽  
Time Motion ◽  
Motion Problem

Parameter optimization of open-loop control of a circular cylinder by simplified reinforcement learning

2021 ◽  
Vol 33 (10) ◽  
pp. 107110
Author(s):  
Pengyu Lai ◽  
Rui Wang ◽  
Wei Zhang ◽  
Hui Xu
Keyword(s):  
Reinforcement Learning ◽  
Circular Cylinder ◽  
Parameter Optimization ◽  
Open Loop ◽  
Open Loop Control ◽  
Loop Control

scholarly journals Experience Weighted Learning in Multiagent Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yi Zou ◽  
Jijuan Zhong ◽  
Zhihao Jiang ◽  
Hong Zhang ◽  
Xuyu Pu
Keyword(s):  
Reinforcement Learning ◽  
Bounded Rationality ◽  
Multiagent Systems ◽  
Parameter Optimization ◽  
Multiagent System ◽  
Learning Algorithm ◽  
Pure Strategy ◽  
Evolutionary Game ◽  
Fictitious Play ◽  
The Impact

Agents face challenges to achieve adaptability and stability when interacting with dynamic counterparts in a complex multiagent system (MAS). To strike a balance between these two goals, this paper proposes a learning algorithm for heterogeneous agents with bounded rationality. It integrates reinforcement learning as well as fictitious play to evaluate the historical information and adopt mechanisms in evolutionary game to adapt to uncertainty, which is referred to as experience weighted learning (EWL) in this paper. We have conducted multiagent simulations to test the performance of EWL in various games. The results demonstrate that the average payoff of EWL exceeds that of the baseline in all 4 games. In addition, we find that most of the EWL agents converge to pure strategy and become stable finally. Furthermore, we test the impact of 2 import parameters, respectively. The results show that the performance of EWL is quite stable and there is a potential to improve its performance by parameter optimization.

scholarly journals Continuous reinforcement learning to adapt multi-objective optimization online for robot motion

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142091149
Author(s):  
Kai Zhang ◽  
Sterling McLeod ◽  
Minwoo Lee ◽  
Jing Xiao
Keyword(s):  
Reinforcement Learning ◽  
Environmental Changes ◽  
Short Term Memory ◽  
Dynamic Environments ◽  
Multi Objective Optimization ◽  
Continuous Reinforcement ◽  
Multi Objective ◽  
Learning Framework ◽  
Time Motion ◽  
Learning Agent

This article introduces a continuous reinforcement learning framework to enable online adaptation of multi-objective optimization functions for guiding a mobile robot to move in changing dynamic environments. The robot with this framework can continuously learn from multiple or changing environments where it encounters different numbers of obstacles moving in unknown ways at different times. Using both planned trajectories from a real-time motion planner and already executed trajectories as feedback observations, our reinforcement learning agent enables the robot to adapt motion behaviors to environmental changes. The agent contains a Q network connected to a long short-term memory network. The proposed framework is tested in both simulations and real robot experiments over various, dynamically varied task environments. The results show the efficacy of online continuous reinforcement learning for quick adaption to different, unknown, and dynamic environments.

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