Learning Trajectories of Hamiltonian Systems with Neural Networks

Modeling of conservative systems with neural networks is an area of active research. A popular approach is to use Hamiltonian neural networks (HNNs) which rely on the assumptions that a conservative system is described with Hamilton's equations of motion. Many recent works focus on improving the integration schemes used when training HNNs. In this work, we propose to enhance HNNs with an estimation of a continuous-time trajectory of the modeled system using an additional neural network, called a deep hidden physics model in the literature. We demonstrate that the proposed integration scheme works well for HNNs, especially with low sampling rates, noisy and irregular observations.

A Grid-Structured Model of Tubular Reactors

We propose a grid-like computational model of tubular reactors. The architecture is inspired by the computations performed by solvers of partial differential equations which describe the dynamics of the chemical process inside a tubular reactor. The proposed model may be entirely based on the known form of the partial differential equations or it may contain generic machine learning components such as multi-layer perceptrons. We show that the proposed model can be trained using limited amounts of data to describe the state of a fixed-bed catalytic reactor. The trained model can reconstruct unmeasured states such as the catalyst activity using the measurements of inlet concentrations and temperatures along the reactor.

A Relational Model for One-Shot Classification

We show that a deep learning model with built-in relational inductive bias can bring benefits to sample-efficient learning, without relying on extensive data augmentation. The proposed one-shot classification model performs relational matching of a pair of inputs in the form of local and pairwise attention. Our approach solves perfectly the one-shot image classification Omniglot challenge. Our model exceeds human level accuracy, as well as the previous state of the art, with no data augmentation.

Automating Privilege Escalation with Deep Reinforcement Learning

AI-based defensive solutions are necessary to defend networks and information assets against intelligent automated attacks. Gathering enough realistic data for training machine learning-based defenses is a significant practical challenge. An intelligent red teaming agent capable of performing realistic attacks can alleviate this problem. However, there is little scientific evidence demonstrating the feasibility of fully automated attacks using machine learning. In this work, we exemplify the potential threat of malicious actors using deep reinforcement learning to train automated agents. We present an agent that uses a state-of-the-art reinforcement learning algorithm to perform local privilege escalation. Our results show that the autonomous agent can escalate privileges in a Windows 7 environment using a wide variety of different techniques depending on the environment configuration it encounters. Hence, our agent is usable for generating realistic attack sensor data for training and evaluating intrusion detection systems.

Learning to Assist Agents by Observing Them

The ability of an AI agent to assist other agents, such as humans, is an important and challenging goal, which requires the assisting agent to reason about the behavior and infer the goals of the assisted agent. Training such an ability by using reinforcement learning usually requires large amounts of online training, which is difficult and costly. On the other hand, offline data about the behavior of the assisted agent might be available, but is non-trivial to take advantage of by methods such as offline reinforcement learning. We introduce methods where the capability to create a representation of the behavior is first pre-trained with offline data, after which only a small amount of interaction data is needed to learn an assisting policy. We test the setting in a gridworld where the helper agent has the capability to manipulate the environment of the assisted artificial agents, and introduce three different scenarios where the assistance considerably improves the performance of the assisted agents.

Behaviour-conditioned policies for cooperative reinforcement learning tasks

The cooperation among AI systems, and between AI systems and humans is becoming increasingly important. In various real-world tasks, an agent needs to cooperate with unknown partner agent types. This requires the agent to assess the behaviour of the partner agent during a cooperative task and to adjust its own policy to support the cooperation. Deep reinforcement learning models can be trained to deliver the required functionality but are known to suffer from sample inefficiency and slow learning. However, adapting to a partner agent behaviour during the ongoing task requires ability to assess the partner agent type quickly. We suggest a method, where we synthetically produce populations of agents with different behavioural patterns together with ground truth data of their behaviour, and use this data for training a meta-learner. We additionally suggest an agent architecture, which can efficiently use the generated data and gain the meta-learning capability. When an agent is equipped with such a meta-learner, it is capable of quickly adapting to cooperation with unknown partner agent types in new situations. This method can be used to automatically form a task distribution for meta-training from emerging behaviours that arise, for example, through self-play.

Medical SANSformers: Training self-supervised transformers without attention for Electronic Medical Records

We leverage deep sequential models to tackle the problem of predicting healthcare utilization for patients, which could help governments to better allocate resources for future healthcare use. Specifically, we study the problem of \textit{divergent subgroups}, wherein the outcome distribution in a smaller subset of the population considerably deviates from that of the general population. The traditional approach for building specialized models for divergent subgroups could be problematic if the size of the subgroup is very small (for example, rare diseases). To address this challenge, we first develop a novel attention-free sequential model, SANSformers, instilled with inductive biases suited for modeling clinical codes in electronic medical records. We then design a task-specific self-supervision objective and demonstrate its effectiveness, particularly in scarce data settings, by pre-training each model on the entire health registry (with close to one million patients) before fine-tuning for downstream tasks on the divergent subgroups. We compare the novel SANSformer architecture with the LSTM and Transformer models using two data sources and a multi-task learning objective that aids healthcare utilization prediction. Empirically, the attention-free SANSformer models perform consistently well across experiments, outperforming the baselines in most cases by at least $\sim 10$\%. Furthermore, the self-supervised pre-training boosts performance significantly throughout, for example by over $\sim 50$\% (and as high as $800$\%) on $R^2$ score when predicting the number of hospital visits.

End-to-End Learning of Keypoint Representations for Continuous Control from Images

In many control problems that include vision, optimal controls can be inferred from the location of the objects in the scene. This information can be represented using keypoints, which is a list of spatial locations in the input image. Previous works show that keypoint representations learned during unsupervised pre-training using encoder-decoder architectures can provide good features for control tasks. In this paper, we show that it is possible to learn efficient keypoint representations end-to-end, without the need for unsupervised pre-training, decoders, or additional losses. Our proposed architecture consists of a differentiable keypoint extractor that feeds the coordinates of the estimated keypoints directly to a soft actor-critic agent. The proposed algorithm yields performance competitive to the state-of-the art on DeepMind Control Suite tasks.

Learning to Play Imperfect-Information Games by Imitating an Oracle Planner

We consider learning to play multiplayer imperfect-information games with simultaneous moves and large state-action spaces. Previous attempts to tackle such challenging games have largely focused on model-free learning methods, often requiring hundreds of years of experience to produce competitive agents. Our approach is based on model-based planning. We tackle the problem of partial observability by first building an (oracle) planner that has access to the full state of the environment and then distilling the knowledge of the oracle to a (follower) agent which is trained to play the imperfect-information game by imitating the oracle's choices. We experimentally show that planning with naive Monte Carlo tree search does not perform very well in large combinatorial action spaces. We therefore propose planning with a fixed-depth tree search and decoupled Thompson sampling for action selection. We show that the planner is able to discover efficient playing strategies in the games of Clash Royale and Pommerman and the follower policy successfully learns to implement them by training on a few hundred battles.

Regularizing Model-Based Planning with Energy-Based Models

Model-based reinforcement learning could enable sample-efficient learning by quickly acquiring rich knowledge about the world and using it to improve behaviour without additional data. Learned dynamics models can be directly used for planning actions but this has been challenging because of inaccuracies in the learned models. In this paper, we focus on planning with learned dynamics models and propose to regularize it using energy estimates of state transitions in the environment. We visually demonstrate the effectiveness of the proposed method and show that off-policy training of an energy estimator can be effectively used to regularize planning with pre-trained dynamics models. Further, we demonstrate that the proposed method enables sample-efficient learning to achieve competitive performance in challenging continuous control tasks such as Half-cheetah and Ant in just a few minutes of experience.