Learning control policies using deep reinforcement learning has shown great success for a variety of applications, including robotics and automated driving. A key area limiting the adaptation of RL in the real world is the lack of trust in the decision-making process of such policies. Therefore, explainability is a requirement of any RL agent operating in the real world. In this work, we propose a family of control policies that are explainable-by-design regarding individual observation components on object-based scene representations. By estimating diagonal squashed Gaussian and categorical mixture distributions on sub-spaces of the decomposed observations, we develop stochastic policies with easy-to-read explanations of the decision-making process. Our design is generally applicable to any RL algorithm using stochastic policies. We showcase the explainability on an extensive suite of single-and multi-agent simulations, set-and sequence-based high-level scenes, and discrete and continuous action spaces, with performance at least on-par or better compared to standard policy architectures. In additional experiments, we analyze the robustness of our approach to its single additional hyper-parameter and examine its potential for very low computational requirements with tiny policies.

The combination of self-play and planning has achieved great successes in sequential games, for instance in Chess and Go. However, adapting algorithms such as AlphaZero to simultaneous games poses a new challenge. In these games, missing information about concurrent actions of other agents is a limiting factor as they may select different Nash equilibria or do not play optimally at all. Thus, it is vital to model the behavior of the other agents when interacting with them in simultaneous games. To this end, we propose Albatross: AlphaZero for Learning Bounded-rational Agents and Temperature-based Response Optimization using Simulated Self-play. Albatross learns to play the novel equilibrium concept of a Smooth Best Response Logit Equilibrium (SBRLE), which enables cooperation and competition with agents of any playing strength. We perform an extensive evaluation of Albatross on a set of cooperative and competitive simultaneous perfect-information games. In contrast to AlphaZero, Albatross is able to exploit weak agents in the competitive game of Battlesnake. Additionally, it yields an improvement of 37.6% compared to previous state of the art in the cooperative Overcooked benchmark.

This article presents Wor(l)d-GAN, a method to perform data-driven procedural content generation via machine learning inMinecraftfrom a single example. Based on a 3-D generative adversarial network (GAN) architecture, we are able to create arbitrarily sized world snippets from a given sample. Our method applies dense representations used in natural language processing in two ways. First, we proposeblock2vecrepresentations based onword2vec. Second, we use the pretrained large language model bidirectional encoder representations from transformers (BERT) to generate representations directly from the token names. These representations make Wor(l)d-GAN independent of the number of different blocks, which can vary a lot inMinecraft, and enable the generation of larger levels. We evaluate our approach on creations from the community as well as structures generated with theMinecraftWorld Generator under several metrics. Wor(l)d-GAN enables its users to generateMinecraftworlds based on parts of their creations.

Deep convolutional neural networks require large amounts of labeled data samples. For many real-world applications, this is a major limitation which is commonly treated by augmentation methods. In this work, we address the problem of learning deep neural networks on small datasets. Our proposed architecture called ChimeraMix learns a data augmentation by generating compositions of instances. The generative model encodes images in pairs, combines the features guided by a mask, and creates new samples. For evaluation, all methods are trained from scratch without any additional data. Several experiments on benchmark datasets, e.g. ciFAIR-10, STL-10, and ciFAIR-100, demonstrate the superior performance of ChimeraMix compared to current state-of-the-art methods for classification on small datasets. Code is available at https://github.com/creinders/ChimeraMix.

This work presentsToken-basedOne-shotArbitraryDimensionGenerativeAdversarialNetwork (TOAD-GAN), a novel procedural content generation algorithm that generates token-based video game levels from only one example. We show that the created levels can be of arbitrary size, and the patterns of the training levels are well captured. The method can be extended with user interaction during the generation process to achieve certain token layouts and interpretations of the same base level by different generators. Our method is further evaluated with an extensive ablation study and level similarity metrics on theSuper Mario Bros.benchmark. Finally, we extend our method to mix the style of multiple input levels, turning it into a framework for few-shot level generation.

This work introduces World-GAN, the first method to perform data-driven Procedural Content Generation via Machine Learning in Minecraft from a single example. Based on a 3D Generative Adversarial Network (GAN) architecture, we are able to create arbitrarily sized world snippets from a given sample. We evaluate our approach on creations from the community as well as structures generated with the Minecraft World Generator. Our method is motivated by the dense representations used in Natural Language Processing (NLP) introduced with word2vec [1]. The proposed block2vec representations make World-GAN independent from the number of different blocks, which can vary a lot in Minecraft, and enable the generation of larger levels. Finally, we demonstrate that changing this new representation space allows us to change the generated style of an already trained generator. World-GAN enables its users to generate Minecraft worlds based on parts of their creations.