activity
20232026
most citedDistill Knowledge in Multi-task Reinforcement Learning with Optimal-Transport Regularization

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026

Low Variance Trust Region Optimization with Independent Actors and Sequential Updates in Cooperative Multi-agent Reinforcement Learning

Bang Giang Le, Viet Cuong Ta

Cooperative multi-agent reinforcement learning assumes each agent shares the same reward function and can be trained effectively using the Trust Region framework of single-agent. I…

cs.LG2026

Explicit Credit Assignment through Local Rewards and Dependence Graphs in Multi-Agent Reinforcement Learning

Bang Giang Le, Viet Cuong Ta

To promote cooperation in Multi-Agent Reinforcement Learning, the reward signals of all agents can be aggregated together, forming global rewards that are commonly known as the ful…

cs.AI2025

Resolve Highway Conflict in Multi-Autonomous Vehicle Controls with Local State Attention

Xuan Duy Ta, Bang Giang Le, Thanh Ha Le +1

In mixed-traffic environments, autonomous vehicles must adapt to human-controlled vehicles and other unusual driving situations. This setting can be framed as a multi-agent reinfor…

cs.LG2025

Enhance Exploration in Safe Reinforcement Learning with Contrastive Representation Learning

Duc Kien Doan, Bang Giang Le, Viet Cuong Ta

In safe reinforcement learning, agent needs to balance between exploration actions and safety constraints. Following this paradigm, domain transfer approaches learn a prior Q-funct…

cs.LG2024

Toward Finding Strong Pareto Optimal Policies in Multi-Agent Reinforcement Learning

Bang Giang Le, Viet Cuong Ta

In this work, we study the problem of finding Pareto optimal policies in multi-agent reinforcement learning problems with cooperative reward structures. We show that any algorithm…

cs.LG2023★ 1 cited

Distill Knowledge in Multi-task Reinforcement Learning with Optimal-Transport Regularization

Bang Giang Le, Viet Cuong Ta

In multi-task reinforcement learning, it is possible to improve the data efficiency of training agents by transferring knowledge from other different but related tasks. Because the…