Results 11 to 20 of about 66,383 (257)
SYNTHETIC LEARNING ENVIRONMENT: DESIGN FEATURES
Інформаційні технології і засоби навчання, 2018 The article discusses the transformation of teaching in the transition from traditional material-object environment to learning in a digital synthetic environment. Attention is paid to the fact that, to date, students prefer online and mixed learning, in
Evgeniy P. Popechitelev +1 more
doaj +1 more source
Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies
CoRR, 2021 This work explores learning agent-agnostic synthetic environments (SEs) for Reinforcement Learning. SEs act as a proxy for target environments and allow agents to be trained more efficiently than when directly trained on the target environment. We formulate this as a bi-level optimization problem and represent an SE as a neural network.
Fabio Ferreira +2 more
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Learning to Localize in New Environments from Synthetic Training Data [PDF]
2021 IEEE International Conference on Robotics and Automation (ICRA), 2021 Most existing approaches for visual localization either need a detailed 3D model of the environment or, in the case of learning-based methods, must be retrained for each new scene. This can either be very expensive or simply impossible for large, unknown environments, for example in search-and-rescue scenarios.
Winkelbauer, Dominik +2 more
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Learning Synthetic Environments and Reward Networks for Reinforcement Learning
CoRR, 2022 We introduce Synthetic Environments (SEs) and Reward Networks (RNs), represented by neural networks, as proxy environment models for training Reinforcement Learning (RL) agents. We show that an agent, after being trained exclusively on the SE, is able to solve the corresponding real environment. While an SE acts as a full proxy to a real environment by
Fabio Ferreira +3 more
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Boosting Deep Reinforcement Learning Agents with Generative Data Augmentation
Applied Sciences, 2023 Data augmentation is a promising technique in improving exploration and convergence speed in deep reinforcement learning methodologies. In this work, we propose a data augmentation framework based on generative models for creating completely novel states
Tasos Papagiannis +2 more
doaj +1 more source
Applied Sciences, 2022 This paper presents a novel approach to training a real-world object detection system based on synthetic data utilizing state-of-the-art technologies. Training an object detection system can be challenging and time-consuming as machine learning requires ...
Ingeborg Rasmussen +4 more
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Reinforcement Learning in an Environment Synthetically Augmented with Digital Pheromones [PDF]
Advances in Artificial Intelligence, 2014 Reinforcement learning requires information about states, actions, and outcomes as the basis for learning. For many applications, it can be difficult to construct a representative model of the environment, either due to lack of required information or because of that the model's state space may become too large to allow a solution in a reasonable ...
Salvador E. Barbosa, Mikel D. Petty
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Asynchronous Deep Double Dueling Q-learning for trading-signal execution in limit order book markets
Frontiers in Artificial Intelligence, 2023 We employ deep reinforcement learning (RL) to train an agent to successfully translate a high-frequency trading signal into a trading strategy that places individual limit orders.
Peer Nagy +4 more
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Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022 Typos corrected ...
Matthew Finlayson +3 more
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Producing Synthetic Dataset for Human Fall Detection in AR/VR Environments
Applied Sciences, 2021 Human poses and the behaviour estimation for different activities in (virtual reality/augmented reality) VR/AR could have numerous beneficial applications.
Denis Zherdev +5 more
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