Search Results for author:
Found 7 papers, 1 papers with code
Formation Control for Moving Target Enclosing via Relative Localization
Based on the generation strategy of desired formation pattern and relative localization estimates, a cooperative formation tracking control scheme is proposed, which enables the formation geometric center to asymptotically converge to the moving target.
EASpace: Enhanced Action Space for Policy Transfer
In this paper, a novel algorithm named EASpace (Enhanced Action Space) is proposed, which formulates macro actions in an alternative form to accelerate the learning process using multiple available sub-optimal expert policies.
A Tutorial on Linear Least Square Estimation
This is a brief tutorial on the least square estimation technique that is straightforward yet effective for parameter estimation.
Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning
It is shown by numerical simulations that the proposed hybrid design outperforms the pursuit policies either learned from vanilla reinforcement learning or designed by the potential field method.
Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control
More importantly, the existing multi-agent reinforcement learning (MARL) algorithms cannot ensure the closed-loop stability of a multi-agent system from a control-theoretic perspective, so the learned control polices are highly possible to generate abnormal or dangerous behaviors in real applications.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles
This paper presents a novel model-reference reinforcement learning algorithm for the intelligent tracking control of uncertain autonomous surface vehicles with collision avoidance.
Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties
With the conventional control, we can ensure the learning-based control law provides closed-loop stability for the overall system, and potentially increase the sample efficiency of the deep reinforcement learning.
