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Found 13 papers, 1 papers with code
3D Guidance Law for Maximal Coverage and Target Enclosing with Inherent Safety
In this paper, we address the problem of enclosing an arbitrarily moving target in three dimensions by a single pursuer, which is an unmanned aerial vehicle (UAV), for maximum coverage while also ensuring the pursuer's safety by preventing collisions with the target.
Self-organizing Multiagent Target Enclosing under Limited Information and Safety Guarantees
The proposed control eliminates the need for a fixed or pre-established agent arrangement around the target and requires only relative information between an agent and the target.
Rating-based Reinforcement Learning
This paper develops a novel rating-based reinforcement learning approach that uses human ratings to obtain human guidance in reinforcement learning.
Fairness in Preference-based Reinforcement Learning
Toward this objective, we design a new fairness-induced preference-based reinforcement learning or FPbRL.
Generalized Maximum Entropy Reinforcement Learning via Reward Shaping
We also show the addition of the agent’s policy entropy at the next state yields new soft Q function and state value function that are concise and modular.
Human-guided Robot Behavior Learning: A GAN-assisted Preference-based Reinforcement Learning Approach
To reduce and minimize the need for human queries, we propose a new GAN-assisted human preference-based reinforcement learning approach that uses a generative adversarial network (GAN) to actively learn human preferences and then replace the role of human in assigning preferences.
Graph Based Multi-layer K-means++ (G-MLKM) for Sensory Pattern Analysis in Constrained Spaces
Based on the dual graph and graph theory, we then generalize MLKM to G-MLKM by first understanding local data-target association and then extracting cross-local data-target association mathematically analyze the data association at intersections of that space.
Learn to Exceed: Stereo Inverse Reinforcement Learning with Concurrent Policy Optimization
In this paper, we study the problem of obtaining a control policy that can mimic and then outperform expert demonstrations in Markov decision processes where the reward function is unknown to the learning agent.
Deep Model Compression Via Two-Stage Deep Reinforcement Learning
Besides accuracy, the model size of convolutional neural networks (CNN) models is another important factor considering limited hardware resources in practical applications.
Relationship Explainable Multi-objective Optimization Via Vector Value Function Based Reinforcement Learning
Solving multi-objective optimization problems is important in various applications where users are interested in obtaining optimal policies subject to multiple, yet often conflicting objectives.
Relationship Explainable Multi-objective Reinforcement Learning with Semantic Explainability Generation
We demonstrate the effectiveness of the proposed approach via a MuJoCo based robotics case study.
Multi-Objective Reinforcement Learning
reinforcement-learning
A Multi-Layer K-means Approach for Multi-Sensor Data Pattern Recognition in Multi-Target Localization
Data-target association is an important step in multi-target localization for the intelligent operation of un- manned systems in numerous applications such as search and rescue, traffic management and surveillance.
Multi-Sensor Data Pattern Recognition for Multi-Target Localization: A Machine Learning Approach
Data-target pairing is an important step towards multi-target localization for the intelligent operation of unmanned systems.
