Results 21 to 30 of about 1,166,366 (314)
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
doaj +1 more source
Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies
, 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.
Ferreira, Fabio +2 more
openaire +2 more sources
Scientific Reports, 2022 Site-specific treatment of weeds in agricultural landscapes has been gaining importance in recent years due to economic savings and minimal impact on the environment.
Bishwa B. Sapkota +6 more
semanticscholar +1 more source
Learning Synthetic Environments and Reward Networks for Reinforcement Learning
, 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
Ferreira, Fabio +3 more
openaire +2 more sources
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario [PDF]
The Web Conference, 2019 Traffic signal control is an emerging application scenario for reinforcement learning. Besides being as an important problem that affects people's daily life in commuting, traffic signal control poses its unique challenges for reinforcement learning in ...
Huichu Zhang +9 more
semanticscholar +1 more source
Learning From Synthetic Data for Crowd Counting in the Wild [PDF]
Computer Vision and Pattern Recognition, 2019 Recently, counting the number of people for crowd scenes is a hot topic because of its widespread applications (e.g. video surveillance, public security).
Qi Wang, Junyu Gao, Wei Lin, Yuan Yuan
semanticscholar +1 more source
The Physics of Fluids, 2023 In the current paper, the zero-mass synthetic jet flow control combined with a proximal policy optimization (PPO) algorithm in deep reinforcement learning is constructed, and a policy transfer strategy which is trained in two-dimensional (2D) environment
semanticscholar +1 more source
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
doaj +1 more source
Structure learning enhances concept formation in synthetic Active Inference agents
PLoS ONE, 2022 Humans display astonishing skill in learning about the environment in which they operate. They assimilate a rich set of affordances and interrelations among different elements in particular contexts, and form flexible abstractions (i.e., concepts) that ...
Victorita Neacsu +3 more
semanticscholar +1 more source