31 citations · 36 across the 11 of their papers we have counts for
8 papers · 1 filter
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning
Xiaobai Ma, David Isele, Jayesh K. Gupta +2
Multi-agent reinforcement learning (MARL) provides an efficient way for simultaneously learning policies for multiple agents interacting with each other. However, in scenarios requ…
Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships
Xiaobai Ma, Jiachen Li, Mykel J. Kochenderfer +2
Deep reinforcement learning (DRL) provides a promising way for learning navigation in complex autonomous driving scenarios. However, identifying the subtle cues that can indicate d…
Safe Reinforcement Learning on Autonomous Vehicles
David Isele, Alireza Nakhaei, Kikuo Fujimura
There have been numerous advances in reinforcement learning, but the typically unconstrained exploration of the learning process prevents the adoption of these methods in many safe…
Uncertainty-Aware Data Aggregation for Deep Imitation Learning
Yuchen Cui, David Isele, Scott Niekum +1
Estimating statistical uncertainties allows autonomous agents to communicate their confidence during task execution and is important for applications in safety-critical domains suc…
Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios
Yeping Hu, Alireza Nakhaei, Masayoshi Tomizuka +1
In order to drive safely and efficiently under merging scenarios, autonomous vehicles should be aware of their surroundings and make decisions by interacting with other road partic…
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
Jiachen Yang, Alireza Nakhaei, David Isele +2
A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success. This multi-goal multi-agent setting poses…