Learning Temporally Extended Skills in Continuous Domains as Symbolic Actions for Planning

Problems which require both long-horizon planning and continuous control capabilities pose significant challenges to existing reinforcement learning agents. In this paper we introduce a novel hierarchical reinforcement learning agent which links temporally extended skills for continuous control with a forward model in a symbolic discrete abstraction of the environment's state for planning. We term our agent SEADS for Symbolic Effect-Aware Diverse Skills. We formulate an objective and corresponding algorithm which leads to unsupervised learning of a diverse set of skills through intrinsic motivation given a known state abstraction. The skills are jointly learned with the symbolic forward model which captures the effect of skill execution in the state abstraction. After training, we can leverage the skills as symbolic actions using the forward model for long-horizon planning and subsequently execute the plan using the learned continuous-action control skills. The proposed algorithm learns skills and forward models that can be used to solve complex tasks which require both continuous control and long-horizon planning capabilities with high success rate. It compares favorably with other flat and hierarchical reinforcement learning baseline agents and is successfully demonstrated with a real robot.Comment: Project website (including video) is available at https://seads.is.tue.mpg.de/. (v2) Accepted for publication at the 6th Conference on Robot Learning (CoRL) 2022, Auckland, New Zealand. (v3) Added details on checkpointing (S.8.1), with references on p.7, p.8, p.21 to clarify number of env. steps of reported result

Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings

We introduce a novel suite of state-of-the-art bilingual text embedding models that are designed to support English and another target language. These models are capable of processing lengthy text inputs with up to 8192 tokens, making them highly versatile for a range of natural language processing tasks such as text retrieval, clustering, and semantic textual similarity (STS) calculations. By focusing on bilingual models and introducing a unique multi-task learning objective, we have significantly improved the model performance on STS tasks, which outperforms the capabilities of existing multilingual models in both target language understanding and cross-lingual evaluation tasks. Moreover, our bilingual models are more efficient, requiring fewer parameters and less memory due to their smaller vocabulary needs. Furthermore, we have expanded the Massive Text Embedding Benchmark (MTEB) to include benchmarks for German and Spanish embedding models. This integration aims to stimulate further research and advancement in text embedding technologies for these languages

CamiloAguilarL/dqn-supply-chain: Version Final Proyecto de grado

No description provided

Single Photon Counting X-ray Imaging with Si and CdTe Single Chip Pixel Detectors and Multichip Pixel Modules

Multichip modules (MCM) with 4 single photon counting MPEC 2.3 chips bump bonded to 1.3 cm x 1.3 cm large CdTe and Si semiconductor sensors as well as to single chip pixel detectors have been successfully built and operated. The MPEC 2.3 chip provides a pixel count rate up to 1 MHz with a large dynamic range of 18 bit, 2 counters and energy windowing with continuously adjustable thresholds. Each MPEC has 32 x 32 pixels of 200 um x 200 um pixel size. For a MCM the 4 chips are arranged in a 2 x 2 array which leads to a 64 x 64 sensor pixel geometry. The MCM construction is described, and the imaging performance of the different detectors is shown. As readout system a newly developed USB system has been used.Comment: 6 pages, 14 figures, IEEE NSS 2003 conference paper, submitted to IEEE TN

Gymnasium: a standard interface for reinforcement learning environments

Gymnasium is an open-source library providing an API for reinforcement learning environments. Its main contribution is a central abstraction for wide interoperability between benchmark environments and training algorithms. Gymnasium comes with various built-in environments and utilities to simplify researchers' work along with being supported by most training libraries. This paper outlines the main design decisions for Gymnasium, its key features, and the differences to alternative APIs

Gymnasium

An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)</span