8 citations · 13 across the 7 of their papers we have counts for
4 papers · 1 filter
SLOPE: Optimistic Potential Landscape Shaping for Model-based Reinforcement Learning
Yao-Hui Li, Zeyu Wang, Xin Li +7
Model-based reinforcement learning (MBRL) is sample-efficient but struggles in sparse reward settings. A critical bottleneck arises from the lack of informative gradients in sparse…
Revisiting Bisimulation Metric for Robust Representations in Reinforcement Learning
Leiji Zhang, Zeyu Wang, Xin Li +1
Bisimulation metric has long been regarded as an effective control-related representation learning technique in various reinforcement learning tasks. However, in this paper, we ide…
Learning Fused State Representations for Control from Multi-View Observations
Zeyu Wang, Yao-Hui Li, Xin Li +3
Multi-View Reinforcement Learning (MVRL) seeks to provide agents with multi-view observations, enabling them to perceive environment with greater effectiveness and precision. Recen…
Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion
Ethan Pronovost, Meghana Reddy Ganesina, Noureldin Hendy +4
Automated creation of synthetic traffic scenarios is a key part of validating the safety of autonomous vehicles (AVs). In this paper, we propose Scenario Diffusion, a novel diffusi…