Proceedings of the seventh international conference (1990) on Machine learning
Learning to Perceive and Act by Trial and Error
Machine Learning
Reinforcement learning of non-Markov decision processes
Artificial Intelligence - Special volume on computational research on interaction and agency, part 2
Machine Learning - Special issue on reinforcement learning
Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Scalable Internal-State Policy-Gradient Methods for POMDPs
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Exact and approximate algorithms for partially observable markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
Model-free reinforcement learning as mixture learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Action representation and partially observable planning using epistemic logic
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Thinking with external representations
AI & Society
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Intrinsically Motivated Learning in Natural and Artificial Systems
Intrinsically Motivated Learning in Natural and Artificial Systems
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Passive sensory processing is often insufficient to guide biological organisms in complex environments. Rather, behaviourally relevant information can be accessed by performing so-called epistemicactions that explicitly aim at unveiling hidden information. However, it is still unclear how an autonomous agent can learn epistemic actions and how it can use them adaptively. In this work, we propose a definition of epistemic actions for POMDPs that derive from their characterizations in cognitive science and classical planning literature. We give theoretical insights about how partial observability and epistemic actions can affect the learning process and performance in the extreme conditions of model-free and memory-free reinforcement learning where hidden information cannot be represented. We finally investigate these concepts using an integrated eye-arm neural architecture for robot control, which can use its effectors to execute epistemic actions and can exploit the actively gathered information to efficiently accomplish a seek-and-reach task.