A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.




Go-explore: a new approach for hard-exploration problems

Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, Jeff Clune

arXiv, 2019

Introduces Go-Explore, an exploration algorithm capable of solving the grand-challenge hard-exploration Atari games Montezuma′s Revenge and Pitfall.

Download here

Reinforcement Learning Under Moral Uncertainty

Adrien Ecoffet, Joel Lehman

arXiv, 2020

An ambitious goal for artificial intelligence is to create agents that behave ethically. Unfortunately, there is widespread disagreement about which ethical theory an agent should follow. This paper translates philosophical work on moral uncertainty into an RL algorithm and investigates how moral uncertainty can avoid extreme behavior compared to single theories.

Download here

Estimating Q(s,s’) with Deep Deterministic Dynamics Gradients

Ashley D. Edwards, Himanshu Sahni, Rosanne Liu, Jane Hung, Ankit Jain, Rui Wang, Adrien Ecoffet, Thomas Miconi, Charles Isbell, Jason Yosinski

ICML, 2020

In this paper, we introduce a novel form of value function, Q(s, s′). We highlight the benefits of this approach in terms of value function transfer, learning within redundant action spaces, and learning off-policy from state observations generated by sub-optimal or completely random policies.

Download here

Exploration Based Language Learning for Text-Based Games

Andrea Madotto, Mahdi Namazifar, Joost Huizinga, Piero Molino, Adrien Ecoffet, Huaixiu Zheng, Alexandros Papangelis, Dian Yu, Chandra Khatri, Gokhan Tur

IJCAI, 2020

This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games using the Go-Explore exploration algorithm.

Download here

First return, then explore

Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, Jeff Clune

Nature, 2021

The definite version of the Go-Explore algorithm. On top of the results from the original pre-print, it introduces a dynamic representation that supports all Atari games, a variant in which the exploration phase can be performed in stochastic environments, and demonstrates Go-Explore working in a robotics environment.

Download here