Simulation-based reinforcement learning for real-world autonomous driving
B Osiński, A Jakubowski, P Zięcina… - … on robotics and …, 2020 - ieeexplore.ieee.org
We use reinforcement learning in simulation to obtain a driving system controlling a full-size
real-world vehicle. The driving policy takes RGB images from a single camera and their …
real-world vehicle. The driving policy takes RGB images from a single camera and their …
Deepracer: Autonomous racing platform for experimentation with sim2real reinforcement learning
DeepRacer is a platform for end-to-end experimentation with RL and can be used to
systematically investigate the key challenges in developing intelligent control systems …
systematically investigate the key challenges in developing intelligent control systems …
Deepracer: Educational autonomous racing platform for experimentation with sim2real reinforcement learning
DeepRacer is a platform for end-to-end experimentation with RL and can be used to
systematically investigate the key challenges in developing intelligent control systems …
systematically investigate the key challenges in developing intelligent control systems …
Virtual to real reinforcement learning for autonomous driving
Reinforcement learning is considered as a promising direction for driving policy learning.
However, training autonomous driving vehicle with reinforcement learning in real …
However, training autonomous driving vehicle with reinforcement learning in real …
Vision-based autonomous car racing using deep imitative reinforcement learning
Autonomous car racing is a challenging task in the robotic control area. Traditional modular
methods require accurate mapping, localization and planning, which makes them …
methods require accurate mapping, localization and planning, which makes them …
Learning robust control policies for end-to-end autonomous driving from data-driven simulation
A Amini, I Gilitschenski, J Phillips… - IEEE Robotics and …, 2020 - ieeexplore.ieee.org
In this work, we present a data-driven simulation and training engine capable of learning
end-to-end autonomous vehicle control policies using only sparse rewards. By leveraging …
end-to-end autonomous vehicle control policies using only sparse rewards. By leveraging …
Reinforcement learning for autonomous driving with latent state inference and spatial-temporal relationships
Deep reinforcement learning (DRL) provides a promising way for learning navigation in
complex autonomous driving scenarios. However, identifying the subtle cues that can …
complex autonomous driving scenarios. However, identifying the subtle cues that can …
Driving in dense traffic with model-free reinforcement learning
Traditional planning and control methods could fail to find a feasible trajectory for an
autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle …
autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle …
End-to-end model-free reinforcement learning for urban driving using implicit affordances
M Toromanoff, E Wirbel… - Proceedings of the IEEE …, 2020 - openaccess.thecvf.com
Reinforcement Learning (RL) aims at learning an optimal behavior policy from its own
experiments and not rule-based control methods. However, there is no RL algorithm yet …
experiments and not rule-based control methods. However, there is no RL algorithm yet …
Deep reinforcement learning framework for autonomous driving
Reinforcement learning is considered to be a strong AI paradigm which can be used to
teach machines through interaction with the environment and learning from their mistakes …
teach machines through interaction with the environment and learning from their mistakes …