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Option Discovery for Autonomous Generation of Symbolic Knowledge
In this work we present an empirical study where we demonstrate the possibility of developing an artificial agent that is capable to autonomously explore an experimental scenario.
Learning High-Level Planning Symbols from Intrinsically Motivated Experience
In particular, the architecture first acquires options in a fully autonomous fashion on the basis of open-ended learning, then builds a PDDL domain based on symbols and operators that can be used to accomplish user-defined goals through a standard PDDL planner.
Hierarchical Reinforcement Learning
Vocal Bursts Intensity Prediction
