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Reward Function Design Method for Long Episode Pursuit Tasks Under Polar Coordinate in Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning has recently been applied to solve pursuit problems. However, it suffers from a large number of time steps per...
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Capturing Reward Functions for Autonomous Driving: Smooth Feedbacks, Random Explorations and Explanation-Based Learning
End-to-end reinforcement learning methods recently has yielded successful models [9, 12, 14, 16] in simulation environments such as CARLA [7]. These...
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A reward-based performability modelling of a fault-tolerant safety–critical system
Nowadays, various computer system carries out critical functions. The failure of these systems leads to unacceptable loss. Such systems are called...
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Self-Adaptive LSAC-PID Approach Based on Lyapunov Reward Sha** for Mobile Robots
In order to solve the control problem of multiple-input multiple-output (MIMO) systems in complex and variable control environments, a model-free...
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Dynamic Weight-based Multi-Objective Reward Architecture for Adaptive Traffic Signal Control System
An Adaptive Traffic Signal Control (ATSC) system uses real-time traffic information to control traffic lights and makes the public transport system...
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Predicting the Reward System of Knowledge Sharing in the Industrialized Engineering Sector Based on Regulatory Mechanisms
Limitations of knowledge sharing within companies are a major cause of unsatisfactory productivity within industrialized engineering production...
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A novel tri-stage with reward-switching mechanism for constrained multiobjective optimization problems
The effective exploitation of infeasible solutions plays a crucial role in addressing constrained multiobjective optimization problems (CMOPs)....
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Navigation of Mobile Robots Based on Deep Reinforcement Learning: Reward Function Optimization and Knowledge Transfer
This paper presents an end-to-end online learning navigation method based on deep reinforcement learning (DRL) for mobile robots, whose objective is...
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Faster Robotic Arm Movement Planning via Guided Attenuation Reward Sha**
The expensive learning cost has become a serious problem in robotic arm movement planning using reinforcement learning method. A significant amount...
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Achieving Goals Using Reward Sha** and Curriculum Learning
Real-time control for robotics is a popular research area in the reinforcement learning community. Through the use of techniques such as reward...
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Integrated energy optimization scheduling with active/passive demand response and reward and punishment ladder carbon trading
With the increase in energy demand and carbon emission requirements, energy management and CO 2 emission reduction on the user side are significant...
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Accelerated Reward Policy (ARP) for Robotics Deep Reinforcement Learning
Reward policy is a crucial part for Deep Reinforcement Learning (DRL) applications in Robotics. The challenges for autonomous systems with...
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Optimizing Reward Function Weights and Enhancing Control Mechanisms for Bipedal Robots Using LSTM and Attention Mechanisms
This paper introduces an optimized control approach for bipedal robots, merging Bayesian optimization for reward function weights and a novel neural...
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Penalty-Reward Analysis with Uninorms: A Study of Customer (Dis)Satisfaction
In customer (dis)satisfaction research, analytic methods are needed to capture the complex relationship between overall (dis)satisfaction with a...
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A Second-Order Adaptive Network Model for Collective Emotional Response During Reward-Based Gaming
In this paper, a second-order adaptive self-modelling network model is introduced to model collective emotional response during frequent repetitive...
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Two-stage reward allocation with decay for multi-agent coordinated behavior for sequential cooperative task by using deep reinforcement learning
We propose a two-stage reward allocation method with decay using an extension of replay memory to adapt this rewarding method for deep reinforcement...
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Evaluation of Safe Reinforcement Learning with CoMirror Algorithm in a Non-Markovian Reward Problem
In reinforcement learning, an agent in an environment improves the skill depending on a reward, which is the feedback from an environment. For...
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Multi-Agent Reward-Iteration Fuzzy Q-Learning
Fuzzy Q-learning extends Q-learning to continuous state space and has been applied to a wide range of applications such as robot control. But in a...
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Path Planning for Parafoil Airdrop System Based on TD3 Algorithm: Reward Sha** with Potential Field
The flight time of the parafoil airdrop system is limited by the release altitude, and how to accurately guide the parafoil landing within the...
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Spatial Consciousness Model of Intrinsic Reward in Partially Observable Environments
In reinforcement learning navigation, agent exploration based on intrinsic rewards has uncertainties, including observation, action, and neural...