Search Results for author: Claire Glanois

Found 7 papers, 5 papers with code

HyperNCA: Growing Developmental Networks with Neural Cellular Automata

1 code implementation25 Apr 2022 Elias Najarro, Shyam Sudhakaran, Claire Glanois, Sebastian Risi

In contrast to deep reinforcement learning agents, biological neural networks are grown through a self-organized developmental process.

reinforcement-learning

Neuro-Symbolic Hierarchical Rule Induction

no code implementations26 Dec 2021 Claire Glanois, Xuening Feng, Zhaohui Jiang, Paul Weng, Matthieu Zimmer, Dong Li, Wulong Liu

We propose an efficient interpretable neuro-symbolic model to solve Inductive Logic Programming (ILP) problems.

Inductive logic programming reinforcement-learning

A Survey on Interpretable Reinforcement Learning

no code implementations24 Dec 2021 Claire Glanois, Paul Weng, Matthieu Zimmer, Dong Li, Tianpei Yang, Jianye Hao, Wulong Liu

To that aim, we distinguish interpretability (as a property of a model) and explainability (as a post-hoc operation, with the intervention of a proxy) and discuss them in the context of RL with an emphasis on the former notion.

Autonomous Driving Decision Making +1

Growing 3D Artefacts and Functional Machines with Neural Cellular Automata

1 code implementation15 Mar 2021 Shyam Sudhakaran, Djordje Grbic, Siyan Li, Adam Katona, Elias Najarro, Claire Glanois, Sebastian Risi

Neural Cellular Automata (NCAs) have been proven effective in simulating morphogenetic processes, the continuous construction of complex structures from very few starting cells.

Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning

3 code implementations17 Dec 2020 Matthieu Zimmer, Claire Glanois, Umer Siddique, Paul Weng

As a solution method, we propose a novel neural network architecture, which is composed of two sub-networks specifically designed for taking into account the two aspects of fairness.

Fairness Multi-agent Reinforcement Learning +1

EvoCraft: A New Challenge for Open-Endedness

1 code implementation8 Dec 2020 Djordje Grbic, Rasmus Berg Palm, Elias Najarro, Claire Glanois, Sebastian Risi

In contrast to previous work in Minecraft that focused on learning to play the game, the grand challenge we pose here is to automatically search for increasingly complex artifacts in an open-ended fashion.

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