4 papers
V-SHARE: Provable Convergence Guarantees for Advantage-based Algorithms
Kintan Saha, Hamed Masoudi, Xinyi Sheng +3
In reinforcement learning, advantage-based value-learning methods, unlike standard Q-learning, share information across actions by decomposing the action-value function into a stat…
Ergodicity in reinforcement learning
Dominik Baumann, Erfaun Noorani, Arsenii Mustafin +5
In reinforcement learning, we typically aim to optimize the expected value of the sum of rewards an agent collects over a trajectory. However, if the process generating these rewar…
Revisiting Value Iteration: Unified Analysis of Discounted and Average-Reward Cases
Arsenii Mustafin, Xinyi Sheng, Dominik Baumann
While Value Iteration (VI) is one of the most fundamental algorithms in Reinforcement Learning, its theoretical convergence guarantees still exhibit a persistent mismatch with empi…
Beyond expected value: geometric mean optimization for long-term policy performance in reinforcement learning
Xinyi Sheng, Dominik Baumann
Reinforcement learning (RL) algorithms typically optimize the expected cumulative reward, i.e., the expected value of the sum of scalar rewards an agent receives over the course of…