A system for a machine reinforcement learning architecture for an environment\nwith a\nplurality of agents includes: at least one memory and at least one processor\nconfigured to\nprovide a multi-agent reinforcement learning architecture, the multi-agent\nreinforcement\nlearning model based on a ...
Quantitative research is the most widely spread application of data science in Marketing or financial domain where applicability of state of the art reinforcement learning for auto-learning is less explored paradigm. Reinforcement learning is heavily dependent on having a simulated environment which is ...
This paper studies the flexible double shop scheduling problem (FDSSP) that considers simultaneously job shop and assembly shop. It brings about the problem of scheduling association of the related tasks. To this end, a reinforcement learning algorithm w
To address these problems, this paper proposes an intelligent capture algorithm based on the PPO algorithm with the A2C framework, as the reinforcement learning algorithm requires no model of the robot. Collision detection is introduced into the training so that the strategy network obtained from the...
In this paper, we introduce a model-bases reinforcement learning method called H-learning, which optimizes undiscounted average reward. We compare it with three other reinforcement learning methods in the domain of scheduling Automatic Guided Vehicles, and transportation robots used in modern manufacturin...
The following will describe the research status of each method Overall framework of deep reinforcement learning for process planning The overall framework of this paper is shown in Fig. 1. This framework consists of two main parts: the problem description and mathematical modeling of process planning...
Reinforcement learning is a field of machine learning that discovers optimal behavior policies through trial and error29,30. Reinforcement learning learns the mapping from environmental states to actions, with the goal of maximizing the cumulative reward value obtained by actions from the environment2. ...
supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. The core of the machine learning techniques is associated with the training and testing of data, where the large amount of data are usually split into at least two parts. A series of criteria are ...
A mobile robot path planning method based on improved deep reinforcement learning is proposed. First, in order to conform to the actual kinematics model of the robot, the continuous environmental sta...
Deep reinforcement learning (DRL) integrates the advantages of the perception of deep learning and enables reinforcement learning scale to problems with high dimensional state and action spaces that were previously intractable. The success of DRL primarily relies on the high level representation ability ...