This paper examines a variety of classical optimization problems, including well-known minimization tasks and more general variational inequalities. We consider a stochastic formulation of these problems and, unlike most previous work, we take into account the complex Markov nature of the noise. We also consider the geometry of the problem in an arbitrary non-Euclidean setting and propose four methods based on the Mirror Descent iteration technique. The theoretical analysis is provided for smooth and convex minimization problems and variational inequalities with Lipschitz and monotone operators. The convergence guarantees obtained are optimal for first-order stochastic methods, as evidenced by the lower bound estimates provided in this paper. In order to validate the theoretical results, we present the relevant numerical experiments on various reinforcement learning tasks.

Multi-agent reinforcement learning (MARL) has recently excelled in solving challenging cooperative and competitive multi-agent problems in various environments, typically involving a small number of agents and full observability. Moreover, a range of crucial robotics-related tasks, such as multi-robot pathfinding, which have traditionally been approached with classical non-learnable methods (e.g., heuristic search), are now being suggested for solution using learning-based or hybrid methods. However, in this domain, it remains difficult, if not impossible, to conduct a fair comparison between classical, learning-based, and hybrid approaches due to the lack of a unified framework that supports both learning and evaluation. To address this, we introduce POGEMA, a comprehensive set of tools that includes a fast environment for learning, a problem instance generator, a collection of predefined problem instances, a visualization toolkit, and a benchmarking tool for automated evaluation. We also introduce and define an evaluation protocol that specifies a range of domain-related metrics, computed based on primary evaluation indicators (such as success rate and path length), enabling a fair multi-fold comparison. The results of this comparison, which involves a variety of state-of-the-art MARL, search-based, and hybrid methods, are presented.

The prevalence of information manipulation online has created a need for propaganda detection systems.Such systems have typically focused on the surface words, ignoring the linguistic structure.Here we aim to bridge this gap.In particular, we present the first attempt at using discourse analysis for the task.We consider both paragraph-level and token-level classification and we propose a discourse-aware Transformer architecture.Our experiments on English and Russian demonstrate sizeable performance gains compared to a number of baselines.Moreover, our ablation study emphasizes the importance of specific types of discourse features, and our in-depth analysis reveals a strong correlation between propaganda instances and discourse spans.

The rate of scientific publications is increasing exponentially, necessitating a significant investment of time in order to read and comprehend the most important articles.While ancillary services exist to facilitate this process, they are typically closed-model and paid services or have limited capabilities.In this paper, we present PaperPersiChat, an open chatbotsystem designed for the discussion of scientific papers.This system supports summarization and question-answering modes within a single end-to-end chatbot pipeline, which is guided by discourse analysis.To expedite the development of similar systems, we also release the gathered dataset, which has no publicly available analogues.

Recent transformer-based approaches to multiparty conversation generation may produce syntactically coherent but discursively inconsistent dialogues in some cases.To address this issue, we propose an approach to integrate a dialogue act planning stage into the end-to-end transformer-based generation pipeline.This approach consists of a transformer fine-tuning procedure based on linearized dialogue representations that include special discourse tokens.The obtained results demonstrate that incorporating discourse tokens into training sequences is sufficient to significantly improve dialogue consistency and overall generation quality.The suggested approach performs well, including for automatically annotated data.Apart from that, it is observed that increasing the weight of the discourse planning task in the loss function accelerates learning convergence.

Recent Transformer-based approaches to NLG like GPT-2 can generate syntactically coherent original texts. However, these generated texts have serious flaws. One of them is a global discourse incoherence. We present an approach to estimate the quality of discourse structure. Empirical results confirm that the discourse structure of currently generated texts is inaccurate. We propose the research directions to plan it and fill in the text in its leaves using the pipeline consisting of two GPT-based generation models. The suggested approach is universal and can be applied to different languages.