Statecharts: A visual formalism for complex systems
Science of Computer Programming
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Planning and Control in Artificial Intelligence: A Unifying Perspective
Applied Intelligence
An Analysis of Direct Reinforcement Learning in Non-Markovian Domains
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Decision-Theoretic, High-Level Agent Programming in the Situation Calculus
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
On the Existence of Fixed Points for Q-Learning and Sarsa in Partially Observable Domains
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Reinforcement learning for POMDPs based on action values and stochastic optimization
Eighteenth national conference on Artificial intelligence
Exact and approximate algorithms for partially observable markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
The utility of temporal abstraction in reinforcement learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Concurrent hierarchical reinforcement learning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
LearnPNP: a tool for learning agent behaviors
RoboCup 2010
Reinforcement learning through global stochastic search in N-MDPs
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Induction and learning of finite-state controllers from simulation
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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Agent programming in complex, partially observable and stochastic domains usually requires a great deal of understanding of both the domain and the task, in order to provide the agent with the knowledge necessary to act effectively. While symbolic methods allow the designer to specify declarative knowledge about the domain, the resulting plan can be brittle since it is difficult to supply a symbolic model that is accurate enough to foresee all possible events in complex environments, especially in the case of partial observability. Reinforcement Learning (RL) techniques, on the other hand, can learn a policy and make use of a learned model, but it is difficult to reduce and shape the scope of the learning algorithm by exploiting a priori information. We propose a methodology for writing complex agent programs that can be effectively improved through experience. We show how to derive a stochastic process from a partial specification of the plan, so that the latter's perfomance can be improved solving a RL problem much smaller than classical RL formulations. Finally, we demonstrate our approach in the context of Keepaway Soccer, a common RL benchmark based on a RoboCup Soccer 2D simulator.