Neuroevolution of Self-Interpretable Agents

Inattentional blindness is the psychological phenomenon that causes one to miss things in plain sight. It is a consequence of the selective attention in perception that lets us remain focused on important parts of our world without distraction from irrelevant details. Motivated by selective attention, we study the properties of artificial agents that perceive the world through the lens of a self-attention bottleneck. By constraining access to only a small fraction of the visual input, we show that their policies are directly interpretable in pixel space. We find neuroevolution ideal for training self-attention architectures for vision-based reinforcement learning (RL) tasks, allowing us to incorporate modules that can include discrete, non-differentiable operations which are useful for our agent. We argue that self-attention has similar properties as indirect encoding, in the sense that large implicit weight matrices are generated from a small number of key-query parameters, thus enabling our agent to solve challenging vision based tasks with at least 1000x fewer parameters than existing methods. Since our agent attends to only task critical visual hints, they are able to generalize to environments where task irrelevant elements are modified while conventional methods fail. Videos of our results and source code available at https://attentionagent.github.io/Comment: To appear at the Genetic and Evolutionary Computation Conference (GECCO 2020) as a full pape

Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP Problems

Rapid online adaptation to changing tasks is an important problem in machine learning and, recently, a focus of meta-reinforcement learning. However, reinforcement learning (RL) algorithms struggle in POMDP environments because the state of the system, essential in a RL framework, is not always visible. Additionally, hand-designed meta-RL architectures may not include suitable computational structures for specific learning problems. The evolution of online learning mechanisms, on the contrary, has the ability to incorporate learning strategies into an agent that can (i) evolve memory when required and (ii) optimize adaptation speed to specific online learning problems. In this paper, we exploit the highly adaptive nature of neuromodulated neural networks to evolve a controller that uses the latent space of an autoencoder in a POMDP. The analysis of the evolved networks reveals the ability of the proposed algorithm to acquire inborn knowledge in a variety of aspects such as the detection of cues that reveal implicit rewards, and the ability to evolve location neurons that help with navigation. The integration of inborn knowledge and online plasticity enabled fast adaptation and better performance in comparison to some non-evolutionary meta-reinforcement learning algorithms. The algorithm proved also to succeed in the 3D gaming environment Malmo Minecraft.Comment: 9 pages. Accepted as a full paper in the Genetic and Evolutionary Computation Conference (GECCO 2020

From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI

This paper reviews the field of Game AI, which not only deals with creating agents that can play a certain game, but also with areas as diverse as creating game content automatically, game analytics, or player modelling. While Game AI was for a long time not very well recognized by the larger scientific community, it has established itself as a research area for developing and testing the most advanced forms of AI algorithms and articles covering advances in mastering video games such as StarCraft 2 and Quake III appear in the most prestigious journals. Because of the growth of the field, a single review cannot cover it completely. Therefore, we put a focus on important recent developments, including that advances in Game AI are starting to be extended to areas outside of games, such as robotics or the synthesis of chemicals. In this article, we review the algorithms and methods that have paved the way for these breakthroughs, report on the other important areas of Game AI research, and also point out exciting directions for the future of Game AI

Neuroevolution trajectory networks : illuminating the evolution of artificial neural networks

Neuroevolution is the discipline whereby ANNs are automatically generated using EC. This field began with the evolution of dense (shallow) neural networks for reinforcement learning task; neurocontrollers capable of evolving specific behaviours as required. Since then, neuroevolution has been used to discover architectures and hyperparameters of Deep Neural Networks, in ways never before conceived by human experts, with many achieving state-of-the-art results. Similar to other types of EAs, there is a wide variety of neuroevolution algorithms constantly being introduced. However, there is a lack of effective tools to examine these systems and assess whether they share underlying principles. This thesis proposes Neuroevolution Trajectory Networks (NTNs), an advanced visualisation tool that leverages complex networks to explore the intrinsic mechanisms inherent in the evolution of neural networks. In this research the tool was developed as a specialised version of Search Trajectory Networks, and it was particularly instantiated to illuminate the behaviour of algorithms navigating neuroevolution search spaces. Throughout the progress, this technique has been progressively applied from systems of shallow network evolution, to deep neural networks. The examination has focused on explicit characteristics of neuroevolution system. Specifically, the learnings achieved highlighted the importance of understanding the role of recombination in neuroevolution, revealing critical inefficiencies that hinder overall algorithm performance. A relation between neurocontrollers' diversity and exploration exists, as topological structures can influence the behavioural characterisations and the diversity generation of different search strategies. Furthermore, our analytical tool has offered insights into the favoured dynamics of transfer learning paradigm in the deep neuroevolution of Convolutional Neural Networks; shedding light on promising avenues for further research and development. All of the above have offered substantial evidence that this advanced tool can be regarded as a specialised observational technique to better understand the inner mechanics of neuroevolution and its specific components, beyond the assessment of accuracy and performance alone. This is done so that collective efforts can be concentrated on aspects that can further enhance the evolution of neural networks. Illuminating their search spaces can be seen as a first step to analysing neural network compositions

Evolutionary design of deep neural networks

Mención Internacional en el título de doctorFor three decades, neuroevolution has applied evolutionary computation to the optimization of the topology of artificial neural networks, with most works focusing on very simple architectures. However, times have changed, and nowadays convolutional neural networks are the industry and academia standard for solving a variety of problems, many of which remained unsolved before the discovery of this kind of networks. Convolutional neural networks involve complex topologies, and the manual design of these topologies for solving a problem at hand is expensive and inefficient. In this thesis, our aim is to use neuroevolution in order to evolve the architecture of convolutional neural networks. To do so, we have decided to try two different techniques: genetic algorithms and grammatical evolution. We have implemented a niching scheme for preserving the genetic diversity, in order to ease the construction of ensembles of neural networks. These techniques have been validated against the MNIST database for handwritten digit recognition, achieving a test error rate of 0.28%, and the OPPORTUNITY data set for human activity recognition, attaining an F1 score of 0.9275. Both results have proven very competitive when compared with the state of the art. Also, in all cases, ensembles have proven to perform better than individual models. Later, the topologies learned for MNIST were tested on EMNIST, a database recently introduced in 2017, which includes more samples and a set of letters for character recognition. Results have shown that the topologies optimized for MNIST perform well on EMNIST, proving that architectures can be reused across domains with similar characteristics. In summary, neuroevolution is an effective approach for automatically designing topologies for convolutional neural networks. However, it still remains as an unexplored field due to hardware limitations. Current advances, however, should constitute the fuel that empowers the emergence of this field, and further research should start as of today.This Ph.D. dissertation has been partially supported by the Spanish Ministry of Education, Culture and Sports under FPU fellowship with identifier FPU13/03917. This research stay has been partially co-funded by the Spanish Ministry of Education, Culture and Sports under FPU short stay grant with identifier EST15/00260.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: María Araceli Sanchís de Miguel.- Secretario: Francisco Javier Segovia Pérez.- Vocal: Simon Luca

Evolution of evolvability for neuroevolution

Scientists have been in awe of the powers of evolution since comprehending the fundamental principles of natural selection. As Darwin eloquently put it, nature has given rise to “endless forms most beautiful”. However, beyond the mesmerizing beauty of nature, lies an even more fascinating aspect - the creation of human intelligence. Although AI research has produced impressive outcomes in the past decade, the techniques employed to accomplish these outcomes diverge considerably from the natural process of brain evolution. The primary dissimilarity stems from the fact that nature employs the evolution of DNA code, which encodes the instructions for brain development, whereas mainstream AI represents all brain parameters directly. This disparity is noteworthy as the indirect representation utilized by nature confers upon it a potent ability to enhance evolvability over time, a feature that is absent in direct representation. This potential stems from the ability to choose instructions in a manner that renders the brain resistant to change in certain directions while facilitating easy modification in other directions. The present thesis delves into the potential of leveraging indirect encoding and evolvability during the neural network evolution process. To this end, we propose two algorithms, namely Quality Evolvability ES and Evolvability Map Elites, which enable direct selection for evolvability. We conduct an evaluation of the efficacy of these algorithms in the context of robotics locomotion tasks. Additionally, we investigate the necessary conditions for indirect encoding to be useful for learning and conduct experiments to verify our hypothesis in the domain of image recognition task

Meta-Heuristic Optimization Methods for Quaternion-Valued Neural Networks

In recent years, real-valued neural networks have demonstrated promising, and often striking, results across a broad range of domains. This has driven a surge of applications utilizing high-dimensional datasets. While many techniques exist to alleviate issues of high-dimensionality, they all induce a cost in terms of network size or computational runtime. This work examines the use of quaternions, a form of hypercomplex numbers, in neural networks. The constructed networks demonstrate the ability of quaternions to encode high-dimensional data in an efficient neural network structure, showing that hypercomplex neural networks reduce the number of total trainable parameters compared to their real-valued equivalents. Finally, this work introduces a novel training algorithm using a meta-heuristic approach that bypasses the need for analytic quaternion loss or activation functions. This algorithm allows for a broader range of activation functions over current quaternion networks and presents a proof-of-concept for future work

Functional Federated Learning in Erlang (ffl-erl)

The functional programming language Erlang is well-suited for concurrent and distributed applications. Numerical computing, however, is not seen as one of its strengths. The recent introduction of Federated Learning, a concept according to which client devices are leveraged for decentralized machine learning tasks, while a central server updates and distributes a global model, provided the motivation for exploring how well Erlang is suited to that problem. We present ffl-erl, a framework for Federated Learning, written in Erlang, and explore how well it performs in two scenarios: one in which the entire system has been written in Erlang, and another in which Erlang is relegated to coordinating client processes that rely on performing numerical computations in the programming language C. There is a concurrent as well as a distributed implementation of each case. Erlang incurs a performance penalty, but for certain use cases this may not be detrimental, considering the trade-off between conciseness of the language and speed of development (Erlang) versus performance (C). Thus, Erlang may be a viable alternative to C for some practical machine learning tasks.Comment: 16 pages, accepted for publication in the WFLP 2018 conference proceedings; final post-prin