The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Coordinated Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
State abstraction for programmable reinforcement learning agents
Eighteenth national conference on Artificial intelligence
Coordinating Multiple Agents via Reinforcement Learning
Autonomous Agents and Multi-Agent Systems
Relational temporal difference learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Hierarchical multi-agent reinforcement learning
Autonomous Agents and Multi-Agent Systems
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
The Journal of Machine Learning Research
Solving multiagent assignment Markov decision processes
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Integrating organizational control into multi-agent learning
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Heuristic selection of actions in multiagent reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Generalizing plans to new environments in relational MDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Concurrent hierarchical reinforcement learning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Reinforcement learning for MDPs with constraints
ECML'06 Proceedings of the 17th European conference on Machine Learning
Reinforcement Learning: An Introduction
IEEE Transactions on Neural Networks
Distributed relational temporal difference learning
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
| Hi-index | 0.00 |
In this paper, we propose to guide reinforcement learning (RL) with expert coordination knowledge for multi-agent problems managed by a central controller. The aim is to learn to use expert coordination knowledge to restrict the joint action space and to direct exploration towards more promising states, thereby improving the overall learning rate. We model such coordination knowledge as constraints and propose a two-level RL system that utilizes these constraints for online applications. Our declarative approach towards specifying coordination in multi-agent learning allows knowledge sharing between constraints and features (basis functions) for function approximation. Results on a soccer game and a tactical real-time strategy game show that coordination constraints improve the learning rate compared to using only unary constraints. The two-level RL system also outperforms existing single-level approach that utilizes joint action selection via coordination graphs.