Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks

2016-12-12 · Sahand Sharifzadeh, Ioannis Chiotellis, Rudolph Triebel, Daniel Cremers

We propose an inverse reinforcement learning (IRL) approach using Deep Q-Networks to extract the rewards in problems with large state spaces. We evaluate the performance of this approach in a simulation-based autonomous driving scenario. Our results resemble the intuitive relation between the reward function and readings of distance sensors mounted at different poses on the car. We also show that, after a few learning rounds, our simulated agent generates collision-free motions and performs human-like lane change behaviour.

📄 PDF Abstract BibTeX arXiv:1612.03653

Code (0)

등록된 구현이 없습니다.

Tasks

Autonomous Driving reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning

2019-12-17 · ICCV 2021 10 · Sonia Baee, Erfan Pakdamanian, Inki Kim, Lu Feng 외

Inspired by human visual attention, we propose a novel inverse reinforcement learning formulation using Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) for predicting the visual attention of drivers in accid…

Autonomous Vehicles reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Trajectory Modeling via Random Utility Inverse Reinforcement Learning

2021-05-25 · Anselmo R. Pitombeira-Neto, Helano P. Santos, Ticiana L. Coelho da Silva, José Antonio F. de Macedo

We consider the problem of modeling trajectories of drivers in a road network from the perspective of inverse reinforcement learning. Cars are detected by sensors placed on sparsely distributed points on the street netwo…

Bayesian Inference Econometrics reinforcement-learning Reinforcement Learning+2

MIRACLE: Inverse Reinforcement and Curriculum Learning Model for Human-inspired Mobile Robot Navigation

2023-12-06 · Nihal Gunukula, Kshitij Tiwari, Aniket Bera

In emergency scenarios, mobile robots must navigate like humans, interpreting stimuli to locate potential victims rapidly without interfering with first responders. Existing socially-aware navigation algorithms face comp…

Navigate Robot Navigation

Inverse Reinforcement Learning Based Stochastic Driver Behavior Learning

2021-07-01 · Mehmet Fatih Ozkan, Abishek Joseph Rocque, Yao Ma

Drivers have unique and rich driving behaviors when operating vehicles in traffic. This paper presents a novel driver behavior learning approach that captures the uniqueness and richness of human driver behavior in reali…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition

2018-05-29 · NeurIPS 2018 12 · Justin Fu, Avi Singh, Dibya Ghosh, Larry Yang 외

The design of a reward function often poses a major practical challenge to real-world applications of reinforcement learning. Approaches such as inverse reinforcement learning attempt to overcome this challenge, but requ…

continuous-control Continuous Control reinforcement-learning Reinforcement Learning+1