Recent progress in the field of reinforcement learning has been accelerated by virtual environments such as video games, where novel algorithms and ideas can be quickly tested a safe reproducible manner. We introduce Google Research Football Environment, new environment agents are trained to play football an advanced, physics-based 3D simulator. The resulting is challenging, easy use customize, it available under permissive open-source license. In addition, provides support for multiplayer...

Neural networks are prone to adversarial attacks. In general, such attacks deteriorate the quality of input by either slightly modifying most its pixels, or occluding it with a patch. this paper, we propose method that keeps image unchanged and only adds an framing on border image. We show empirically our is able successfully attack state-of-theart methods both video classification problems. Notably, proposed results in universal which very fast at test time. Source code can be found...

Transaction privacy is a hard problem on an account-based blockchain such as Ethereum. While Ben-Sasson et al. presented the Zerocash protocol [BCG+14] decentralized anonymous payment (DAP) scheme standing top of Bitcoin, no study about integration DAP ledger defined in account model was provided. In this paper we aim to fill gap and propose ZETH, adaptation that can be deployed Ethereum without making any change base layer. Our shows not only ZETH could used transfer Ether, currency...

Continual learning (CL) -- the ability to continuously learn, building on previously acquired knowledge is a natural requirement for long-lived autonomous reinforcement (RL) agents. While such agents, one needs balance opposing desiderata, as constraints capacity and compute, not catastrophically forget, exhibit positive transfer new tasks. Understanding right trade-off conceptually computationally challenging, which we argue has led community overly focus catastrophic forgetting. In...

Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained capabilities, as recently showcased by the successful applications of foundation models. However, fine-tuning reinforcement learning (RL) models remains challenge. This work conceptualizes one specific cause poor transfer, accentuated in RL setting interplay between actions and observations: forgetting capabilities. Namely, model deteriorates on state subspace downstream task not visited initial phase...

Recent work has shown that using unlabeled data in semi-supervised learning is not always beneficial and can even hurt generalization, especially when there a class mismatch between the labeled examples. We investigate this phenomenon for image classification on CIFAR-10 ImageNet datasets, with many other forms of domain shifts applied (e.g. salt-and-pepper noise). Our main contribution Split Batch Normalization (Split-BN), technique to improve SSL additional comes from shifted distribution....

The performance and reliability of pitch control systems significantly influences the functional safety wind turbines. In this paper, an observer-based fault detection approach for a class nonlinear systems, which can be modeled as Takagi-Sugeno (TS) fuzzy models, is presented applied to electro mechanical drive To achieve robustness we propose TS observer combined with sliding mode that deals unmeasurable premise variables bounded uncertainties in plant. It shown formulation allows sensor...

The ability of continual learning systems to transfer knowledge from previously seen tasks in order maximize performance on new is a significant challenge for the field, limiting applicability solutions realistic scenarios. Consequently, this study aims broaden our understanding and its driving forces specific case reinforcement learning. We adopt SAC as underlying RL algorithm Continual World suite continuous control tasks. systematically how different components (the actor critic,...

Class-incremental learning (CIL) is a particularly challenging variant of continual learning, where the goal to learn discriminate between all classes presented in an incremental fashion. Existing approaches often suffer from excessive forgetting and imbalance scores assigned that have not been seen together during training. In this study, we introduce novel approach, Prediction Error-based Classification (PEC), which differs traditional discriminative generative classification paradigms....

Language model attacks typically assume one of two extreme threat models: full white-box access to weights, or black-box limited a text generation API. However, real-world APIs are often more flexible than just generation: these expose ``gray-box'' leading new vectors. To explore this, we red-team three functionalities exposed in the GPT-4 APIs: fine-tuning, function calling and knowledge retrieval. We find that fine-tuning on as few 15 harmful examples 100 benign can remove core safeguards...

We consider subsets G of a generalized effect algebra E with 0∈G and such that every interval [0, q]G = q]E ∩ (q ∈ , q ≠ 0) is sub-effect the q]E. give condition on under which sub-generalized E.

Language models exhibit scaling laws, whereby increasing model and dataset size yields predictable decreases in negative log likelihood, unlocking a dazzling array of capabilities. This phenomenon spurs many companies to train ever larger pursuit improved performance. Yet, these are vulnerable adversarial inputs such as ``jailbreaks'' prompt injections that induce perform undesired behaviors, posing growing risk become more capable. Prior work indicates computer vision robust with data...

Recent developments in the field of robot grasping have shown great improvements grasp success rates when dealing with unknown objects. In this work we improve on one most promising approaches, Grasp Quality Convolutional Neural Network (GQ-CNN) trained DexNet 2.0 dataset. We propose a new architecture for GQ-CNN and describe practical that increase model validation accuracy from 92.2% to 95.8% 85.9% 88.0% respectively image-wise object-wise training splits.

Let $T,T^{\prime }$ be weak contractions (in the sense of Sz.-Nagy and Foiaş), $m,m^{\prime minimal functions their $C_0$ parts let $d$ greatest common inner divisor }$. It is proved that space $I(T,T^{\prime })$ all operators intertwining reflexive if only model operator $S(d)$ reflexive. Here means compression unilateral shift onto $H^2\ominus dH^2$. In particular, in finite-dimensional spaces roots polynomials are simple. The paper concluded by an example showing quasisimilarity does not...

Diffusion models have achieved remarkable success in generating high-quality images thanks to their novel training procedures applied unprecedented amounts of data. However, a diffusion model from scratch is computationally expensive. This highlights the need investigate possibility these iteratively, reusing computation while data distribution changes. In this study, we take first step direction and evaluate continual learning (CL) properties models. We begin by benchmarking most common CL...

In the following paper we consider a problem of fault detection for mobile robot. The robot which our work is related to, based on new type steering principle [1]. crucial part system are axle position sensors. A failure one them might result in an interruption operation and/or serious damages to hardware and environment elements. To avoid risk such events, reliable has be implemented. Fault facilitated by incorporating measurements from various sensors located board (incremental encoders,...

In this paper a complete characterization of hyperreflexive operators on finite dimensional Hilbert spaces is given.