[2604.00977] Flow-based Policy With Distributional Reinforcement Learning in Trajectory Optimization

Computer Science > Machine Learning arXiv:2604.00977 (cs) [Submitted on 1 Apr 2026] Title:Flow-based Policy With Distributional Reinforcement Learning in Trajectory Optimization Authors:Ruijie Hao, Longfei Zhang, Yang Dai, Yang Ma, Xingxing Liang, Guangquan Cheng View a PDF of the paper titled Flow-based Policy With Distributional Reinforcement Learning in Trajectory Optimization, by Ruijie Hao and Longfei Zhang and Yang Dai and Yang Ma and Xingxing Liang and Guangquan Cheng View PDF HTML (experimental) Abstract:Reinforcement Learning (RL) has proven highly effective in addressing complex control and decision-making tasks. However, in most traditional RL algorithms, the policy is typically parameterized as a diagonal Gaussian distribution, which constrains the policy from capturing multimodal distributions, making it difficult to cover the full range of optimal solutions in multi-solution problems, and the return is reduced to a mean value, losing its multimodal nature and thus providing insufficient guidance for policy updates. In response to these problems, we propose a RL algorithm termed flow-based policy with distributional RL (FP-DRL). This algorithm models the policy using flow matching, which offers both computational efficiency and the capacity to fit complex distributions. Additionally, it employs a distributional RL approach to model and optimize the entire return distribution, thereby more effectively guiding multimodal policy updates and improving agent perfor...