Learning automata: an introduction
Learning automata: an introduction
Instance-Based Learning Algorithms
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Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Risk-Sensitive Reinforcement Learning
Machine Learning
Practical Reinforcement Learning in Continuous Spaces
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Behavioral Cloning of Student Pilots with Modular Neural Networks
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Reinforcement Learning with Bounded Risk
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Essex Wizards 2001 Team Description
RoboCup 2001: Robot Soccer World Cup V
Case-Based Reasoning: Survey and Future Directions
XPS '99 Proceedings of the 5th Biannual German Conference on Knowledge-Based Systems: Knowledge-Based Systems - Survey and Future Directions
Integrating Guidance into Relational Reinforcement Learning
Machine Learning
Confidence-based policy learning from demonstration using Gaussian mixture models
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Two steps reinforcement learning
International Journal of Intelligent Systems
Multi-thresholded approach to demonstration selection for interactive robot learning
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Evolutionary reinforcement learning of artificial neural networks
International Journal of Hybrid Intelligent Systems - Hybridization of Intelligent Systems
Behavioral Cloning for Simulator Validation
RoboCup 2007: Robot Soccer World Cup XI
A Collaborative Reinforcement Learning Approach to Urban Traffic Control Optimization
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Neuroevolutionary reinforcement learning for generalized helicopter control
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Reinforcement learning for games: failures and successes
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Improving Reinforcement Learning by Using Case Based Heuristics
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Risk-sensitive reinforcement learning applied to control under constraints
Journal of Artificial Intelligence Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Transfer learning in real-time strategy games using hybrid CBR/RL
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
SIMBA: A simulator for business education and research
Decision Support Systems
Learning Autonomous Helicopter Flight with Evolutionary Reinforcement Learning
Computer Aided Systems Theory - EUROCAST 2009
Autonomous Helicopter Aerobatics through Apprenticeship Learning
International Journal of Robotics Research
Metric learning for reinforcement learning agents
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
CBR for state value function approximation in reinforcement learning
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Brief Risk-sensitive and minimax control of discrete-time, finite-state Markov decision processes
Automatica (Journal of IFAC)
Local Feature Weighting in Nearest Prototype Classification
IEEE Transactions on Neural Networks
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In this paper, we consider the important problem of safe exploration in reinforcement learning. While reinforcement learning is well-suited to domains with complex transition dynamics and high-dimensional state-action spaces, an additional challenge is posed by the need for safe and efficient exploration. Traditional exploration techniques are not particularly useful for solving dangerous tasks, where the trial and error process may lead to the selection of actions whose execution in some states may result in damage to the learning system (or any other system). Consequently, when an agent begins an interaction with a dangerous and high-dimensional state-action space, an important question arises; namely, that of how to avoid (or at least minimize) damage caused by the exploration of the state-action space. We introduce the PI-SRL algorithm which safely improves suboptimal albeit robust behaviors for continuous state and action control tasks and which efficiently learns from the experience gained from the environment. We evaluate the proposed method in four complex tasks: automatic car parking, pole-balancing, helicopter hovering, and business management.