4 accepted papers
In offline reinforcement learning (RL), updating the value function with the discrete-time Bellman Equation often encounters challenges due to the limited scope of available data. This limitation stems from the Bellman Equation, which cannot accurately predict the value of unvisited states. To addre…
In this study, we used reinforcement learning (RL) models to invest assets in order to earn returns. The models were trained to interact with a simulated environment based on historical market data and learn trading strategies. However, using deep neural networks based on the returns of each period…
Domain randomization (DR) is widely used in reinforcement learning (RL) to bridge the gap between simulation and reality by maximizing its average returns under the perturbation of environmental parameters. However, even the most complex simulators cannot capture all details in reality due to finite…
In this study, we present a colorization network that generates flat-color icons according to given sketches and semantic colorization styles. Specifically, our network contains a style-structure disentangled colorization module and a normalizing flow. The colorization module transforms a paired ske…