Search Results for author:
Found 4 papers, 0 papers with code
Deep Decentralized Reinforcement Learning for Cooperative Control
In order to collaborate efficiently with unknown partners in cooperative control settings, adaptation of the partners based on online experience is required.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Adaptive Dynamic Programming for Model-free Tracking of Trajectories with Time-varying Parameters
We conclude our paper with an example which demonstrates that our new method successfully learns the optimal tracking controller and outperforms existing approaches in terms of tracking error and cost.
Partner Approximating Learners (PAL): Simulation-Accelerated Learning with Explicit Partner Modeling in Multi-Agent Domains
Mixed cooperative-competitive control scenarios such as human-machine interaction with individual goals of the interacting partners are very challenging for reinforcement learning agents.
Adaptive Optimal Control for Reference Tracking Independent of Exo-System Dynamics
This paper provides for the first time an adaptive optimal control method capable to track reference trajectories not being generated by a time-invariant exo-system.
