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Found 5 papers, 1 papers with code
Modelling crypto markets by multi-agent reinforcement learning
Building on a previous foundation work (Lussange et al. 2020), this study introduces a multi-agent reinforcement learning (MARL) model simulating crypto markets, which is calibrated to the Binance's daily closing prices of $153$ cryptocurrencies that were continuously traded between 2018 and 2022.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Continuous Time Continuous Space Homeostatic Reinforcement Learning (CTCS-HRRL) : Towards Biological Self-Autonomous Agent
Homeostasis is a biological process by which living beings maintain their internal balance.
3D detection of roof sections from a single satellite image and application to LOD2-building reconstruction
We here propose a method for urban 3D reconstruction named KIBS(\textit{Keypoints Inference By Segmentation}), which comprises two novel features: i) a full deep learning approach for the 3D detection of the roof sections, and ii) only one single (non-orthogonal) satellite raster image as model input.
Order book regulatory impact on stock market quality: a multi-agent reinforcement learning perspective
Recent technological developments have changed the fundamental ways stock markets function, bringing regulatory instances to assess the benefits of these developments.
Continuous Homeostatic Reinforcement Learning for Self-Regulated Autonomous Agents
The recently introduced homeostatic regulated reinforcement learning theory (HRRL), by defining within the framework of reinforcement learning a reward function based on the internal state of the agent, makes the link between the theories of drive reduction and reinforcement learning.
