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
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
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
PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Discovering Hierarchy in Reinforcement Learning with HEXQ
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Near-Optimal Reinforcement Learning in Polynominal Time
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Hierarchical control and learning for markov decision processes
Hierarchical control and learning for markov decision processes
Temporal abstraction in reinforcement learning
Temporal abstraction in reinforcement learning
Autonomous discovery of temporal abstractions from interaction with an environment
Autonomous discovery of temporal abstractions from interaction with an environment
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Identifying useful subgoals in reinforcement learning by local graph partitioning
ICML '05 Proceedings of the 22nd international conference on Machine learning
An intrinsic reward mechanism for efficient exploration
ICML '06 Proceedings of the 23rd international conference on Machine learning
Causal Graph Based Decomposition of Factored MDPs
The Journal of Machine Learning Research
A layered approach to learning coordination knowledge in multiagent environments
Applied Intelligence
Hierarchical model-based reinforcement learning: R-max + MAXQ
Proceedings of the 25th international conference on Machine learning
The utility of temporal abstraction in reinforcement learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Anticipations, Brains, Individual and Social Behavior: An Introduction to Anticipatory Systems
Anticipatory Behavior in Adaptive Learning Systems
Subgoal Identification for Reinforcement Learning and Planning in Multiagent Problem Solving
MATES '07 Proceedings of the 5th German conference on Multiagent System Technologies
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Towards competence in autonomous agents
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
State abstraction discovery from irrelevant state variables
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Automatic discovery of subgoals in reinforcement learning using strongly connected components
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Finding and transferring policies using stored behaviors
Autonomous Robots
Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Achievement, affiliation, and power: Motive profiles for artificial agents
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Knowledge of opposite actions for reinforcement learning
Applied Soft Computing
Hierarchical behaviours: getting the most bang for your bit
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
Learning skills in reinforcement learning using relative novelty
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Automatic task decomposition and state abstraction from demonstration
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Transfer in reinforcement learning via shared features
The Journal of Machine Learning Research
Abstraction in Model Based Partially Observable Reinforcement Learning Using Extended Sequence Trees
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Object focused q-learning for autonomous agents
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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We present a new method for automatically creating useful temporal abstractions in reinforcement learning. We argue that states that allow the agent to transition to a different region of the state space are useful subgoals, and propose a method for identifying them using the concept of relative novelty. When such a state is identified, a temporally-extended activity (e.g., an option) is generated that takes the agent efficiently to this state. We illustrate the utility of the method in a number of tasks.