4 accepted papers
Trajectory prediction facilitates effective planning and decision-making, while constrained trajectory prediction integrates regulation into prediction. Recent advances in constrained trajectory prediction focus on structured constraints by constructing optimization objectives. However, handling uns
Reinforcement learning (RL) has emerged as a pivotal technique for fine-tuning large language models (LLMs) on specific tasks. However, prevailing RL fine-tuning methods predominantly rely on PPO and its variants. Though these algorithms are effective in general RL settings, they often exhibit subop…
Due to the high complexity of a football match, the opponents' strategies are variable and unknown. Thus predicting the opponents' future intentions accurately based on current situation is crucial for football players' decision-making. To better anticipate the opponents and learn more effective str…
Sparse reward remains a valuable and challenging problem in multi-agent reinforcement learning (MARL). This paper addresses this issue from a new perspective, i.e., lazy agents. We empirically illustrate how lazy agents damage learning from both exploration and exploitation. Then, we propose a novel…