103 citations · 112 across the 19 of their papers we have counts for
6 papers · 1 filter
CAPS: Context-Aware Priority Sampling for Enhanced Imitation Learning in Autonomous Driving
Hamidreza Mirkhani, Behzad Khamidehi, Ehsan Ahmadi +6
In this paper, we introduce Context-Aware Priority Sampling (CAPS), a novel method designed to enhance data efficiency in learning-based autonomous driving systems. CAPS addresses…
Confidence Aware Inverse Constrained Reinforcement Learning
Sriram Ganapathi Subramanian, Guiliang Liu, Mohammed Elmahgiubi +2
In coming up with solutions to real-world problems, humans implicitly adhere to constraints that are too numerous and complex to be specified completely. However, reinforcement lea…
Augmenting Safety-Critical Driving Scenarios while Preserving Similarity to Expert Trajectories
Hamidreza Mirkhani, Behzad Khamidehi, Kasra Rezaee
Trajectory augmentation serves as a means to mitigate distributional shift in imitation learning. However, imitating trajectories that inadequately represent the original expert da…
Benchmarking Constraint Inference in Inverse Reinforcement Learning
Guiliang Liu, Yudong Luo, Ashish Gaurav +2
When deploying Reinforcement Learning (RL) agents into a physical system, we must ensure that these agents are well aware of the underlying constraints. In many real-world problems…
Learning Soft Constraints From Constrained Expert Demonstrations
Ashish Gaurav, Kasra Rezaee, Guiliang Liu +1
Inverse reinforcement learning (IRL) methods assume that the expert data is generated by an agent optimizing some reward function. However, in many settings, the agent may optimize…
CoachNet: An Adversarial Sampling Approach for Reinforcement Learning
Elmira Amirloo Abolfathi, Jun Luo, Peyman Yadmellat +1
Despite the recent successes of reinforcement learning in games and robotics, it is yet to become broadly practical. Sample efficiency and unreliable performance in rare but challe…