Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Automatically generating abstractions for planning
Artificial Intelligence
Learning hierarchical control structures for multiple tasks and changing environments
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Using relative novelty to identify useful temporal abstractions in reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Dynamic abstraction in reinforcement learning via clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Identifying useful subgoals in reinforcement learning by local graph partitioning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Hierarchical multi-agent reinforcement learning
Autonomous Agents and Multi-Agent Systems
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
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We provide a new probability flow analysis algorithm to automatically identify subgoals in a problem space. Our flow analysis, inspired by preflow-push algorithms, measures the topological structure of the problem space to identify states that connect different subset of state space as the subgoals within linear-time complexity. Then we apply a hybrid approach known as subgoal-based SMDP (semi-Markov Decision Process) that is composed of reinforcement learning and planning based on the identified subgoals to solve the problem in a multiagent environment. The effectiveness of this new method used in a multiagent system is demonstrated and evaluated using a capture-the-flag scenario. We showed also that the cooperative coordination emerged between two agents in the scenario through distributed policy learning.