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Found 7 papers, 5 papers with code
HyperNCA: Growing Developmental Networks with Neural Cellular Automata
In contrast to deep reinforcement learning agents, biological neural networks are grown through a self-organized developmental process.
Neuro-Symbolic Hierarchical Rule Induction
We propose an efficient interpretable neuro-symbolic model to solve Inductive Logic Programming (ILP) problems.
A Survey on Interpretable Reinforcement Learning
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.
Growing 3D Artefacts and Functional Machines with Neural Cellular Automata
Neural Cellular Automata (NCAs) have been proven effective in simulating morphogenetic processes, the continuous construction of complex structures from very few starting cells.
Differentiable Logic Machines
The integration of reasoning, learning, and decision-making is key to build more general AI systems.
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning
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.
EvoCraft: A New Challenge for Open-Endedness
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.
