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A cooperative multi-agent system entitles some independent agents to complete complex tasks through coordination and cooperation. Since the dynamics of physical agents are so complex that the environment of learning is indeed stochastic, the paper introduces the decentralized multi-agent reinforcement learning (MARL) algorithm, named as Decentralized Concurrent Learning with Cooperative Policy Exploration...
In view of high dimension, the difficulty of training, the problem of slow learning speed in the application of BP neural network in mobile robot path planning, an algorithm of reinforcement Q learning based on extreme learning machine (Q-ELM algorithm) is proposed in this paper. Firstly, the characteristic of reinforcement learning is combining the dynamic network with supervised learning, and the...
This paper focuses on the flight path planning process with multi-agent for Unmanned Aerial Vehicle (UAV) based on threats analysis and path length constraint. Path planner agent searches the path with global view considering path length constraint and information collector agent deals with path planning in the zone of threats. Scoring function is presented based on analysis the threats' attributes...
Researchers have created machines which operate autonomously in complex and changing environments. An important problem that has been widely studied is that of autonomous navigation systems, through which attempts have been made to create mechanisms with their own decision making in complex environments. Ideally, an autonomous navigation agent must have an ability to learn while working in its environment...
This paper presents a platform to implement and evaluate a learning by imitation framework which enables humanoid robots to learn hand gestures from human beings. A marker based system is used to capture human motion data. From this data we extract the shoulder and elbow joint angles, which uniquely characterize a particular hand gesture. The proposed imitation learning framework aims to generalize...
Reinforcement learning is the problem faced by an agent that must learn behaviour through trial and error interactions with a dynamic environment that lacks the educational examples. Q-learning is one of the most popular algorithms among the reinforcement learning methods. Artificial neural network, as in reinforcement learning, is a sub-entry of machine learning, which can be applied on real frames,...
Policies play an important role in balancing the trade-off between exploration and exploitation problem in q-learning. Pure exploration degrades the performance of the q-learning but increases the flexibility to adapt in a dynamic environment. On the other hand pure exploitation drives the learning process to locally optimal solutions. In this paper, a single agent foraging task has been modeled incorporating...
A path planning algorithm of robot is proposed based on ensemble algorithm of the learning classifier system, which design fitness function in dynamic environment. The paper derived and proved that ensemble algorithm is convergence and provided a theoretical guarantee for the path planning algorithm. Simulation results also showed that genetic algorithms and learning classifier system combination...
This paper addresses the problem of real-time moving-object detection, classification and tracking in populated and dynamic environments. In this scenario, a mobile robot uses 2D laser range data to recognize, track and avoid moving targets. Most previous approaches either rely on pre-defined data features or off-line training of a classifier for specific data sets, thus eliminating the possibility...
Sliding mode control (SMC) of cleaning robot's mobile manipulator based on neural networks which have nonlinear approximation ability is put forward in this article. The controller reduces inherent chattering phenomenon sharply when the uncertainties and external disturbances are unknown. Structure of sliding mode control and neural networkspsila learning algorithms using Lyapunov theorem are designed...
A major issue for reinforcement learning (RL) applied to robotics is the time required to learn a new skill. While RL has been used to learn mobile robot control in many simulated domains, applications involving learning on real robots are still relatively rare. In this paper, the Least-Squares Policy Iteration (LSPI) reinforcement learning algorithm and a new model-based algorithm Least-Squares Policy...
Dynamic multi-objective optimization (DMO) is one of the most challenging class of optimization problems where the objective functions change over time and the optimization algorithm is required to identify the corresponding Pareto optimal solutions with minimal time lag. DMO has received very little attention in the past and none of the existing multi-objective algorithms perform satisfactorily on...
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