CompILE: Compositional Imitation Learning and Execution

We introduce Compositional Imitation Learning and Execution (CompILE): a framework for learning reusable, variable-length segments of hierarchically-structured behavior from demonstration data. CompILE uses a novel unsupervised, fully-differentiable sequence segmentation module to learn latent encodings of sequential data that can be re-composed and executed to perform new tasks. Once trained, our model generalizes to sequences of longer length and from environment instances not seen during training. We evaluate CompILE in a challenging 2D multi-task environment and a continuous control task, and show that it can find correct task boundaries and event encodings in an unsupervised manner. Latent codes and associated behavior policies discovered by CompILE can be used by a hierarchical agent, where the high-level policy selects actions in the latent code space, and the low-level, task-specific policies are simply the learned decoders. We found that our CompILE-based agent could learn given only sparse rewards, where agents without task-specific policies struggle.Comment: ICML (2019

Recent Advances in Variational Autoencoders With Representation Learning for Biomedical Informatics: A Survey

Variational autoencoders (VAEs) are deep latent space generative models that have been immensely successful in multiple exciting applications in biomedical informatics such as molecular design, protein design, medical image classification and segmentation, integrated multi-omics data analyses, and large-scale biological sequence analyses, among others. The fundamental idea in VAEs is to learn the distribution of data in such a way that new meaningful data with more intra-class variations can be generated from the encoded distribution. The ability of VAEs to synthesize new data with more representation variance at state-of-art levels provides hope that the chronic scarcity of labeled data in the biomedical field can be resolved. Furthermore, VAEs have made nonlinear latent variable models tractable for modeling complex distributions. This has allowed for efficient extraction of relevant biomedical information from learned features for biological data sets, referred to as unsupervised feature representation learning. In this article, we review the various recent advancements in the development and application of VAEs for biomedical informatics. We discuss challenges and future opportunities for biomedical research with respect to VAEs.https://doi.org/10.1109/ACCESS.2020.304830

End to End Learning in Autonomous Driving Systems

Convolutional neural networks have advanced visual perception significantly in recent years. Two major ingredients that enable such a success are the composition of simple modules into a complex network and the end to end optimization. However, such success has not yet revolutionized robotics as much as vision, even if robotics suffer from similar problems as traditional computer vision, i.e. imperfectness of the manual pipeline design of the system. This thesis investigates using end-to-end learning for the autonomous driving system, a concrete robotic application. End to end learning can produce reasonable driving behaviors, even in the complex urban driving scenarios. Representation learning in end-to-end driving models is crucial, and auxiliary vision tasks such as semantic segmentation can help to form a more informative driving representation especially when training data is limited. Naive convolutional neural networks are usually only capable of doing reactive control and can not involve complex reasoning in a particular scenario. This thesis also studies how to handle scene conditioned driving behavior, which goes beyond the capability of reactive control. Alongside the end-to-end structure, learning methods also play a critical role. Imitation learning methods will acquire meaningful behaviors but usually, the robot can not master the skill. Reinforcement learning, on the contrary, either barely learns anything if the environment is too complex, or it can master the skill otherwise. To get the best of both worlds, this thesis proposes an algorithmically unified method to learn from both demonstration data and the environment

Deep Neural Networks and Data for Automated Driving

This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above

Improving Deep Reinforcement Learning Using Graph Convolution and Visual Domain Transfer

Recent developments in Deep Reinforcement Learning (DRL) have shown tremendous progress in robotics control, Atari games, board games such as Go, etc. However, model free DRL still has limited use cases due to its poor sampling efficiency and generalization on a variety of tasks. In this thesis, two particular drawbacks of DRL are investigated: 1) the poor generalization abilities of model free DRL. More specifically, how to generalize an agent\u27s policy to unseen environments and generalize to task performance on different data representations (e.g. image based or graph based) 2) The reality gap issue in DRL. That is, how to effectively transfer a policy learned in a simulator to the real world. This thesis makes several novel contributions to the field of DRL which are outlined sequentially in the following. Among these contributions is the generalized value iteration network (GVIN) algorithm, which is an end-to-end neural network planning module extending the work of Value Iteration Networks (VIN). GVIN emulates the value iteration algorithm by using a novel graph convolution operator, which enables GVIN to learn and plan on irregular spatial graphs. Additionally, this thesis proposes three novel, differentiable kernels as graph convolution operators and shows that the embedding-based kernel achieves the best performance. Furthermore, an improvement upon traditional $n$-step $Q$-learning that stabilizes training for VIN and GVIN is demonstrated. Additionally, the equivalence between GVIN and graph neural networks is outlined and shown that GVIN can be further extended to address both control and inference problems. The final subject which falls under the graph domain that is studied in this thesis is graph embeddings. Specifically, this work studies a general graph embedding framework GEM-F that unifies most of the previous graph embedding algorithms. Based on the contributions made during the analysis of GEM-F, a novel algorithm called WarpMap which outperforms DeepWalk and node2vec in the unsupervised learning settings is proposed. The aforementioned reality gap in DRL prohibits a significant portion of research from reaching the real world setting. The latter part of this work studies and analyzes domain transfer techniques in an effort to bridge this gap. Typically, domain transfer in RL consists of representation transfer and policy transfer. In this work, the focus is on representation transfer for vision based applications. More specifically, aligning the feature representation from source domain to target domain in an unsupervised fashion. In this approach, a linear mapping function is considered to fuse modules that are trained in different domains. Proposed are two improved adversarial learning methods to enhance the training quality of the mapping function. Finally, the thesis demonstrates the effectiveness of domain alignment among different weather conditions in the CARLA autonomous driving simulator