Design of Intelligent Control Systems Based on Hierarchical Stochastic Automata
Design of Intelligent Control Systems Based on Hierarchical Stochastic Automata
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
An Analysis of Direct Reinforcement Learning in Non-Markovian Domains
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
Petri net plans: a formal model for representation and execution of multi-robot plans
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
Improving the performance of complex agent plans through reinforcement learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Machine Learning With AIBO Robots in the Four-Legged League of RoboCup
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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High-level programming of robotic agents requires the use of a representation formalism able to cope with several sources of complexity (e.g. parallel execution, partial observability, exogenous events, etc.) and the ability of the designer to model the domain in a precise way. Reinforcement Learning has proved promising in improving the performance, adaptability and robustness of plans in under-specified domains, although it does not scale well with the complexity of common robotic applications. In this paper we propose to combine an extremely expressive plan representation formalism (Petri Net Plans), with Reinforcement Learning over a stochastic process derived directly from such a plan. The derived process has a significantly reduced search space and thus the framework scales well with the complexity of the domain and allows for actually improving the performance of complex behaviors from experience. To prove the effectiveness of the system, we show how to model and learn the behavior of the robotic agents in the context of Keepaway Soccer (a widely accepted benchmark for RL) and the RoboCup Standard Platform League.