Efficient sampling-based maximum entropy inverse reinforcement learning with application to autonomous driving
In the past decades, we have witnessed significant progress in the domain of autonomous
driving. Advanced techniques based on optimization and reinforcement learning become …
driving. Advanced techniques based on optimization and reinforcement learning become …
Relative entropy inverse reinforcement learning
We consider the problem of imitation learning where the examples, demonstrated by an
expert, cover only a small part of a large state space. Inverse Reinforcement Learning (IRL) …
expert, cover only a small part of a large state space. Inverse Reinforcement Learning (IRL) …
Maximum-likelihood inverse reinforcement learning with finite-time guarantees
Inverse reinforcement learning (IRL) aims to recover the reward function and the associated
optimal policy that best fits observed sequences of states and actions implemented by an …
optimal policy that best fits observed sequences of states and actions implemented by an …
Driving behavior modeling using naturalistic human driving data with inverse reinforcement learning
Driving behavior modeling is of great importance for designing safe, smart, and
personalized autonomous driving systems. In this paper, an internal reward function-based …
personalized autonomous driving systems. In this paper, an internal reward function-based …
Advanced planning for autonomous vehicles using reinforcement learning and deep inverse reinforcement learning
Autonomous vehicles promise to improve traffic safety while, at the same time, increase fuel
efficiency and reduce congestion. They represent the main trend in future intelligent …
efficiency and reduce congestion. They represent the main trend in future intelligent …
Analyzing the suitability of cost functions for explaining and imitating human driving behavior based on inverse reinforcement learning
Autonomous vehicles are sharing the road with human drivers. In order to facilitate
interactive driving and cooperative behavior in dense traffic, a thorough understanding and …
interactive driving and cooperative behavior in dense traffic, a thorough understanding and …
Personalized car following for autonomous driving with inverse reinforcement learning
Driving automation is gradually replacing human driving maneuvers in different applications
such as adaptive cruise control and lane keeping. However, contemporary driving …
such as adaptive cruise control and lane keeping. However, contemporary driving …
Decision making for autonomous driving via augmented adversarial inverse reinforcement learning
Making decisions in complex driving environments is a challenging task for autonomous
agents. Imitation learning methods have great potentials for achieving such a goal …
agents. Imitation learning methods have great potentials for achieving such a goal …
Active exploration for inverse reinforcement learning
Abstract Inverse Reinforcement Learning (IRL) is a powerful paradigm for inferring a reward
function from expert demonstrations. Many IRL algorithms require a known transition model …
function from expert demonstrations. Many IRL algorithms require a known transition model …
[PDF][PDF] Risk-sensitive Inverse Reinforcement Learning via Coherent Risk Models.
A Majumdar, S Singh… - … science and systems, 2017 - m.roboticsproceedings.org
The literature on Inverse Reinforcement Learning (IRL) typically assumes that humans take
actions in order to minimize the expected value of a cost function, ie, that humans are risk …
actions in order to minimize the expected value of a cost function, ie, that humans are risk …