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Found 15 papers, 1 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.
A method for the ethical analysis of brain-inspired AI
The conclusion resulting from the application of this method is that, compared to traditional AI, brain-inspired AI raises new foundational ethical issues and some new practical ethical issues, and exacerbates some of the issues raised by traditional AI.
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.
A Computational Model of Representation Learning in the Brain Cortex, Integrating Unsupervised and Reinforcement Learning
We propose that in the cortex the same reward-based trial-and-error processes might support not only the acquisition of motor representations but also of sensory representations.
Learning Abstract Representations through Lossy Compression of Multi-Modal Signals
A key competence for open-ended learning is the formation of increasingly abstract representations useful for driving complex behavior.
REAL-X -- Robot open-Ended Autonomous Learning Architectures: Achieving Truly End-to-End Sensorimotor Autonomous Learning Systems
The first contribution of this work is to study the challenges posed by the previously proposed benchmark `REAL competition' aiming to foster the development of truly open-ended learning robot architectures.
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.
Optimal Options for Multi-Task Reinforcement Learning Under Time Constraints
Reinforcement learning can greatly benefit from the use of options as a way of encoding recurring behaviours and to foster exploration.
Intrinsic motivations and open-ended learning
There is a growing interest and literature on intrinsic motivations and open-ended learning in both cognitive robotics and machine learning on one side, and in psychology and neuroscience on the other.
A Neurocomputational Account of Flexible Goal-directed Cognition and Consciousness: The Goal-Aligning Representation Internal Manipulation Theory (GARIM)
Goal-directed manipulation of representations is a key element of human flexible behaviour, while consciousness is often related to several aspects of higher-order cognition and human flexibility.
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
Reinforcement Learning
+1
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.
Autonomous discovery of the goal space to learn a parameterized skill
The agent may posit as a task space its whole sensory space (i. e. the space of all possible sensor readings) as the achievable goals will certainly be a subset of this space.
