Technical Note: \cal Q-Learning
Machine Learning
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Autonomous shaping: knowledge transfer in reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Cross-domain transfer for reinforcement learning
Proceedings of the 24th international conference on Machine learning
Transfer via inter-task mappings in policy search reinforcement learning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Value-function-based transfer for reinforcement learning using structure mapping
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Building portable options: skill transfer in reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Recently researchers have introduced methods to develop reusable knowledge in reinforcement learning (RL). In this paper, we define simple principles to combine skills in reinforcement learning. We present a skill combination method that uses trained skills to solve different tasks in a RL domain. Through this combination method, composite skills can be used to express tasks at a high level and they can also be re-used with different tasks in the context of the same problem domains. The method generates an abstract task representation based upon normal reinforcement learning which decreases the information coupling of states thus improving an agent's learning. The experimental results demonstrate that the skills combination method can effectively reduce the learning space, and so accelerate the learning speed of the RL agent. We also show in the examples that different tasks can be solved by combining simple reusable skills.