Robuta

With the advent of large datasets, offline reinforcement learning (ORL) is a promising framework for learning good decision-making policies without interacting...

Learning conditional distributions is challenging because the desired outcome is not a single distribution but multiple distributions that correspond to...

09/19/23 - This paper introduces a novel backup strategy for Monte-Carlo Tree Search (MCTS) designed for highly stochastic and partially obse...

When handling complex remote sensing scenarios, rotational angle information can improve detection accuracy and enhance algorithm robustness, providing support...

Finding a transformation between two unknown probability distributions from samples is crucial for modeling complex data distributions and perform tasks such...