Search Results for author: Adrien Ecoffet

Found 8 papers, 4 papers with code

Estimating Q(s,s') with Deterministic Dynamics Gradients

no code implementations ICML 2020 Ashley Edwards, Himanshu Sahni, Rosanne Liu, Jane Hung, Ankit Jain, Rui Wang, Adrien Ecoffet, Thomas Miconi, Charles Isbell, Jason Yosinski

In this paper, we introduce a novel form of a value function, $Q(s, s')$, that expresses the utility of transitioning from a state $s$ to a neighboring state $s'$ and then acting optimally thereafter.

Transfer Learning

Open Questions in Creating Safe Open-ended AI: Tensions Between Control and Creativity

no code implementations12 Jun 2020 Adrien Ecoffet, Jeff Clune, Joel Lehman

This paper proposes that open-ended evolution and artificial life have much to contribute towards the understanding of open-ended AI, focusing here in particular on the safety of open-ended search.

Artificial Life

Reinforcement Learning Under Moral Uncertainty

1 code implementation8 Jun 2020 Adrien Ecoffet, Joel Lehman

An ambitious goal for machine learning is to create agents that behave ethically: The capacity to abide by human moral norms would greatly expand the context in which autonomous agents could be practically and safely deployed, e. g. fully autonomous vehicles will encounter charged moral decisions that complicate their deployment.

Autonomous Vehicles

First return, then explore

1 code implementation27 Apr 2020 Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, Jeff Clune

The promise of reinforcement learning is to solve complex sequential decision problems autonomously by specifying a high-level reward function only.

Montezuma's Revenge

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

1 code implementation21 Feb 2020 Ashley D. Edwards, Himanshu Sahni, Rosanne Liu, Jane Hung, Ankit Jain, Rui Wang, Adrien Ecoffet, Thomas Miconi, Charles Isbell, Jason Yosinski

In this paper, we introduce a novel form of value function, $Q(s, s')$, that expresses the utility of transitioning from a state $s$ to a neighboring state $s'$ and then acting optimally thereafter.

Imitation Learning Transfer Learning

Go-Explore: a New Approach for Hard-Exploration Problems

3 code implementations30 Jan 2019 Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, Jeff Clune

Go-Explore can also harness human-provided domain knowledge and, when augmented with it, scores a mean of over 650k points on Montezuma's Revenge.

Imitation Learning Montezuma's Revenge

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