33 citations · 42 across the 26 of their papers we have counts for
5 papers · 1 filter
Sim2Signal: Sim-to-Real Benchmarks for Traffic Signal Control
Ferdous Al Rafi, Susrik Mukherjee, Latika Liladhar Dekate +6
Reinforcement learning achieves strong traffic signal control performance in simulation, yet policies trained in simulators often fail once deployed in the real world, a failure kn…
DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning
Hanyang Chen, Anirudh Satheesh, Longchao Da +1
Transferring policies across domains poses a vital challenge in reinforcement learning, due to the dynamics mismatch between the source and target domains. In this paper, we consid…
Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control
Justin Turnau, Longchao Da, Khoa Vo +4
Traffic Signal Control (TSC) is essential for managing urban traffic flow and reducing congestion. Reinforcement Learning (RL) offers an adaptive method for TSC by responding to dy…
A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models
Longchao Da, Justin Turnau, Thirulogasankar Pranav Kutralingam +3
Deep Reinforcement Learning (RL) has been explored and verified to be effective in solving decision-making tasks in various domains, such as robotics, transportation, recommender s…
Probabilistic Offline Policy Ranking with Approximate Bayesian Computation
Longchao Da, Porter Jenkins, Trevor Schwantes +2
In practice, it is essential to compare and rank candidate policies offline before real-world deployment for safety and reliability. Prior work seeks to solve this offline policy r…