Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Dopamine: generalization and bonuses
Neural Networks - Computational models of neuromodulation
Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
An Architecture for Behavior-Based Reinforcement Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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A central role in the development process of children is played by self-exploratory activities Through a playful interaction with the surrounding environment, they test their own capabilities, explore novel situations, and understand how their actions affect the world During this kind of exploration, interesting situations may be discovered By learning to reach these situations, a child incrementally develops more and more complex skills Inspired by studies from psychology, neuroscience, and machine learning, we designed SMILe (Self-Motivated Incremental Learning), a learning framework that allows artificial agents to autonomously identify and learn a set of abilities useful to face several different tasks, through an iterated three phase process: by means of a random exploration of the environment (babbling phase), the agent identifies interesting situations and generates an intrinsic motivation (motivating phase) aimed at learning how to get into these situations (skill acquisition phase) This process incrementally increases the skills of the agent, so that new interesting configurations can be experienced We present results on two gridworld environments to show how SMILe makes it possible to learn skills that enable the agent to perform well and robustly in many different tasks.