Reinforcement learning and its application to control
Reinforcement learning and its application to control
Robot shaping: developing autonomous agents through learning
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning to Drive a Bicycle Using Reinforcement Learning and Shaping
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Action Chaining by a Developmental Robot with a Value System
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Theory and application of reward shaping in reinforcement learning
Theory and application of reward shaping in reinforcement learning
Autonomous shaping: knowledge transfer in reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Interactive robot task training through dialog and demonstration
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Automatic shaping and decomposition of reward functions
Proceedings of the 24th international conference on Machine learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Combining manual feedback with subsequent MDP reward signals for reinforcement learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Teaching a robot to perform task through imitation and on-line feedback
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Reinforcement learning from simultaneous human and MDP reward
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
Learning non-myopically from human-generated reward
Proceedings of the 2013 international conference on Intelligent user interfaces
Teaching agents with human feedback: a demonstration of the TAMER framework
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
Using informative behavior to increase engagement in the tamer framework
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Reinforcement Learning is commonly used for learning tasks in robotics, however, traditional algorithms can take very long training times. Reward shaping has been recently used to provide domain knowledge with extra rewards to converge faster. The reward shaping functions are normally defined in advance by the user and are static. This paper introduces a dynamic reward shaping approach, in which these extra rewards are not consistently given, can vary with time and may sometimes be contrary to what is needed for achieving a goal. In the experiments, a user provides verbal feedback while a robot is performing a task which is translated into additional rewards. It is shown that we can still guarantee convergence as long as most of the shaping rewards given per state are consistent with the goals and that even with fairly noisy interaction the system can still produce faster convergence times than traditional reinforcement learning techniques.