Fast and slow curiosity for high-level exploration in reinforcement learning

Reinforcement Learning, Fast and Slow

(2019) Matthew Botvinick et al.   TRENDS IN COGNITIVE SCIENCES

Efficient deep learning of image denoising using patch complexity local divide and deep conquer

(2019) Inpyo Hong et al.   PATTERN RECOGNITION

Skill-based curiosity for intrinsically motivated reinforcement learning

(2019) Nicolas Bougie et al.   MACHINE LEARNING

The Arcade Learning Environment: An Evaluation Platform for General Agents

(2018) M. G. Bellemare et al.   JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH

Neuroscience-Inspired Artificial Intelligence

(2017) Demis Hassabis et al.   NEURON

Mastering the game of Go with deep neural networks and tree search

(2016) David Silver et al.   NATURE

Human-level control through deep reinforcement learning

(2015) Volodymyr Mnih et al.   NATURE

Active learning of inverse models with intrinsically motivated goal exploration in robots

(2012) Adrien Baranes et al.   ROBOTICS AND AUTONOMOUS SYSTEMS

Abandoning Objectives: Evolution Through the Search for Novelty Alone

(2010) Joel Lehman et al.   EVOLUTIONARY COMPUTATION

An analysis of model-based Interval Estimation for Markov Decision Processes

(2008) Alexander L. Strehl et al.   JOURNAL OF COMPUTER AND SYSTEM SCIENCES