Hierarchical multi-agent reinforcement learning
Proceedings of the fifth international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Solving multiagent assignment Markov decision processes
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Generalizing plans to new environments in relational MDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Scaling model-based average-reward reinforcement learning for product delivery
ECML'06 Proceedings of the 17th European conference on Machine Learning
| Hi-index | 0.00 |
Transfer Learning refers to learning of knowledge in one domain that can be applied to a different domain. In this paper, we view transfer learning as generalization of knowledge in a richer representation language that includes multiple subdomains as parts of the same superdomain. We employ relational templates of different specificity to learn pieces of additive value functions. We show significant transfer of learned knowledge across different subdomains of a real-time strategy game by generalizing the value function using relational templates.