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Found 8 papers, 0 papers with code
Purpose for Open-Ended Learning Robots: A Computational Taxonomy, Definition, and Operationalisation
Overall, the approach enables OEL robots to learn in an autonomous way but also to focus on acquiring goals and skills that meet the purposes of the designers and users.
A Definition of Open-Ended Learning Problems for Goal-Conditioned Agents
A lot of recent machine learning research papers have ``open-ended learning'' in their title.
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.
Autonomous Open-Ended Learning of Tasks with Non-Stationary Interdependencies
Building on previous works, we tackle these crucial issues at the level of decision making (i. e., building strategies to properly select between goals), and we propose a hierarchical architecture that treating sub-tasks selection as a Markov Decision Process is able to properly learn interdependent skills on the basis of intrinsically generated motivations.
Autonomous learning of multiple, context-dependent tasks
These situations pose two challenges: (a) to recognise the different contexts that need different policies; (b) quickly learn the policies to accomplish the same tasks in the new discovered contexts.
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
Autonomous Reinforcement Learning of Multiple Interrelated Tasks
Autonomous multiple tasks learning is a fundamental capability to develop versatile artificial agents that can act in complex environments.
Autonomous Open-Ended Learning of Interdependent Tasks
Autonomy is fundamental for artificial agents acting in complex real-world scenarios.
