Technical Note: \cal Q-Learning
Machine Learning
Reinforcement learning with hierarchies of machines
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning Options in Reinforcement Learning
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Equivalence notions and model minimization in Markov decision processes
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Temporal abstraction in reinforcement learning
Temporal abstraction in reinforcement learning
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Dynamic abstraction in reinforcement learning via clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Metrics for finite Markov decision processes
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Identifying useful subgoals in reinforcement learning by local graph partitioning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Causal Graph Based Decomposition of Factored MDPs
The Journal of Machine Learning Research
Automatic discovery and transfer of MAXQ hierarchies
Proceedings of the 25th international conference on Machine learning
Discovering options from example trajectories
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Transfer via soft homomorphisms
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Using Homomorphisms to transfer options across continuous reinforcement learning domains
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Autonomously learning an action hierarchy using a learned qualitative state representation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
Optimal policy switching algorithms for reinforcement learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Using bisimulation for policy transfer in MDPs
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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Temporally extended actions are usually effective in speeding up reinforcement learning. In this paper we present a mechanism for automatically constructing such actions, expressed as options [24], in a finite Markov Decision Process (MDP). To do this, we compute a bisimulation metric [7] between the states in a small MDP and the states in a large MDP, which we want to solve. The shape of this metric is then used to completely define a set of options for the large MDP. We demonstrate empirically that our approach is able to improve the speed of reinforcement learning, and is generally not sensitive to parameter tuning.