6 papers
Actor-Critic Learning for Extended Mean Field Control with Deterministic Policies
Ziheng Cheng, Xin Guo, Huyên Pham +1
This paper develops a model-free reinforcement learning framework for continuous--time extended mean field control problems, where both the dynamics and reward may depend on the jo…
Entropy Regularization Improves Policy Robustness in Continuous-Time Reinforcement Learning
Jialun Cao, Fernando Acero, David Šiška +1
Entropy regularization is widely used in continuous-time reinforcement learning (RL) to reduce sensitivity to environmental perturbations, yet its robustness benefits lack a rigoro…
A note on convergence of Wasserstein policy optimization
David Šiška, Yufei Zhang
Wasserstein Policy Optimization (WPO) is a recently proposed reinforcement learning algorithm that leverages Wasserstein gradient flows to optimize stochastic policies in continuou…
DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training
Tianhao Hu, Xiangcheng Liu, Yuchun Miao +16
Reinforcement learning (RL) has become a critical paradigm for LLM post-training, yet the rollout phase -- accounting for 50--80% of total step time -- is bottlenecked by skewed ge…
Deterministic Policy Gradient for Reinforcement Learning with Continuous Time and State
Ziheng Cheng, Xin Guo, Yufei Zhang
The theory of continuous-time reinforcement learning (RL) has progressed rapidly in recent years. While the ultimate objective of RL is typically to learn deterministic control pol…
Accuracy of Discretely Sampled Stochastic Policies in Continuous-time Reinforcement Learning
Yanwei Jia, Du Ouyang, Yufei Zhang
Stochastic policies (also known as relaxed controls) are widely used in continuous-time reinforcement learning algorithms. However, executing a stochastic policy and evaluating its…