215 citations · 868 across the 14 of their papers we have counts for
3 papers · 1 filter
First return, then explore
Adrien Ecoffet, Joost Huizinga, Joel Lehman +2
The promise of reinforcement learning is to solve complex sequential decision problems autonomously by specifying a high-level reward function only. However, reinforcement learning…
Scaling MAP-Elites to Deep Neuroevolution
Cédric Colas, Joost Huizinga, Vashisht Madhavan +1
Quality-Diversity (QD) algorithms, and MAP-Elites (ME) in particular, have proven very useful for a broad range of applications including enabling real robots to recover quickly fr…
Exploration Based Language Learning for Text-Based Games
Andrea Madotto, Mahdi Namazifar, Joost Huizinga +7
This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games. Text-based computer games describ…