9 research outputs found
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. Here we propose a new
hypernetwork approach to grow artificial neural networks based on neural
cellular automata (NCA). Inspired by self-organising systems and
information-theoretic approaches to developmental biology, we show that our
HyperNCA method can grow neural networks capable of solving common
reinforcement learning tasks. Finally, we explore how the same approach can be
used to build developmental metamorphosis networks capable of transforming
their weights to solve variations of the initial RL task.Comment: Paper accepted as a conference paper at ICLR 'From Cells to
Societies' workshop 202
Deep Neural Network Architectures For Music Genre Classification
With the recent advancements in technology, many tasks in fields such as computer vision, natural language processing, and signal processing have been solved using deep learning architectures. In the audio domain, these architectures have been used to learn musical features of songs to predict: moods, genres, and instruments. In the case of genre classification, deep learning models were applied to popular datasets--which are explicitly chosen to represent their genres--and achieved state-of-the-art results. However, these results have not been reproduced on less refined datasets. To this end, we introduce an un-curated dataset which contains genre labels and 30-second audio previews for approximately fifteen thousand songs from Spotify. In our work, we focus on solving automatic genre classification using deep learning and crude data. Specifically, we propose deep architectures that learn hierarchical characteristics of music using raw waveform audio rather than preprocessed audio in the form of mel-spectrograms and apply these models to the Spotify dataset. Our experiments show how deep learning architectures using unpolished data can achieve comparable results to previous state-of-the-art music classifiers using filtered data
MarioGPT: Open-Ended Text2Level Generation through Large Language Models
Procedural Content Generation (PCG) algorithms provide a technique to
generate complex and diverse environments in an automated way. However, while
generating content with PCG methods is often straightforward, generating
meaningful content that reflects specific intentions and constraints remains
challenging. Furthermore, many PCG algorithms lack the ability to generate
content in an open-ended manner. Recently, Large Language Models (LLMs) have
shown to be incredibly effective in many diverse domains. These trained LLMs
can be fine-tuned, re-using information and accelerating training for new
tasks. In this work, we introduce MarioGPT, a fine-tuned GPT2 model trained to
generate tile-based game levels, in our case Super Mario Bros levels. We show
that MarioGPT can not only generate diverse levels, but can be text-prompted
for controllable level generation, addressing one of the key challenges of
current PCG techniques. As far as we know, MarioGPT is the first text-to-level
model. We also combine MarioGPT with novelty search, enabling it to generate
diverse levels with varying play-style dynamics (i.e. player paths). This
combination allows for the open-ended generation of an increasingly diverse
range of content
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. Recent developments in NCAs lie in the 2D domain,
namely reconstructing target images from a single pixel or infinitely growing
2D textures. In this work, we propose an extension of NCAs to 3D, utilizing 3D
convolutions in the proposed neural network architecture. Minecraft is selected
as the environment for our automaton since it allows the generation of both
static structures and moving machines. We show that despite their simplicity,
NCAs are capable of growing complex entities such as castles, apartment blocks,
and trees, some of which are composed of over 3,000 blocks. Additionally, when
trained for regeneration, the system is able to regrow parts of simple
functional machines, significantly expanding the capabilities of simulated
morphogenetic systems. The code for the experiment in this paper can be found
at: https://github.com/real-itu/3d-artefacts-nca
Goal-Guided Neural Cellular Automata: Learning to Control Self-Organising Systems
Inspired by cellular growth and self-organization, Neural Cellular Automata
(NCAs) have been capable of "growing" artificial cells into images, 3D
structures, and even functional machines. NCAs are flexible and robust
computational systems but -- similarly to many other self-organizing systems --
inherently uncontrollable during and after their growth process. We present an
approach to control these type of systems called Goal-Guided Neural Cellular
Automata (GoalNCA), which leverages goal encodings to control cell behavior
dynamically at every step of cellular growth. This approach enables the NCA to
continually change behavior, and in some cases, generalize its behavior to
unseen scenarios. We also demonstrate the robustness of the NCA with its
ability to preserve task performance, even when only a portion of cells receive
goal information
Variational Neural Cellular Automata
In nature, the process of cellular growth and differentiation has lead to an
amazing diversity of organisms -- algae, starfish, giant sequoia, tardigrades,
and orcas are all created by the same generative process. Inspired by the
incredible diversity of this biological generative process, we propose a
generative model, the Variational Neural Cellular Automata (VNCA), which is
loosely inspired by the biological processes of cellular growth and
differentiation. Unlike previous related works, the VNCA is a proper
probabilistic generative model, and we evaluate it according to best practices.
We find that the VNCA learns to reconstruct samples well and that despite its
relatively few parameters and simple local-only communication, the VNCA can
learn to generate a large variety of output from information encoded in a
common vector format. While there is a significant gap to the current
state-of-the-art in terms of generative modeling performance, we show that the
VNCA can learn a purely self-organizing generative process of data.
Additionally, we show that the VNCA can learn a distribution of stable
attractors that can recover from significant damage.Comment: ICLR 202
Severe Damage Recovery in Evolving Soft Robots through Differentiable Programming
Biological systems are very robust to morphological damage, but artificial
systems (robots) are currently not. In this paper we present a system based on
neural cellular automata, in which locomoting robots are evolved and then given
the ability to regenerate their morphology from damage through gradient-based
training. Our approach thus combines the benefits of evolution to discover a
wide range of different robot morphologies, with the efficiency of supervised
training for robustness through differentiable update rules. The resulting
neural cellular automata are able to grow virtual robots capable of regaining
more than 80\% of their functionality, even after severe types of morphological
damage.Comment: Genetic Programming and Evolvable Machines (GENP). arXiv admin note:
substantial text overlap with arXiv:2102.0257