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Intrinsically Motivated Hierarchical Policy Learning in Multi-objective Markov Decision Processes
In this work, intrinsically motivated reinforcement learning has been successfully deployed to evolve generic skill sets for learning hierarchical policies to solve multi-objective Markov decision processes.
A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data
Trusted autonomy is the scientific and engineering field to establish the foundations and ground work for developing trusted autonomous systems (robotics and software agents) that can be used in our daily life, and can be integrated with humans seamlessly, naturally and efficiently.
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