[HTML][HTML] Toward human-in-the-loop AI: Enhancing deep reinforcement learning via real-time human guidance for autonomous driving
Due to its limited intelligence and abilities, machine learning is currently unable to handle
various situations thus cannot completely replace humans in real-world applications …
various situations thus cannot completely replace humans in real-world applications …
Human-guided reinforcement learning with sim-to-real transfer for autonomous navigation
Reinforcement learning (RL) is a promising approach in unmanned ground vehicles (UGVs)
applications, but limited computing resource makes it challenging to deploy a well-behaved …
applications, but limited computing resource makes it challenging to deploy a well-behaved …
Learning from active human involvement through proxy value propagation
Learning from active human involvement enables the human subject to actively intervene
and demonstrate to the AI agent during training. The interaction and corrective feedback …
and demonstrate to the AI agent during training. The interaction and corrective feedback …
Prioritized experience-based reinforcement learning with human guidance for autonomous driving
Reinforcement learning (RL) requires skillful definition and remarkable computational efforts
to solve optimization and control problems, which could impair its prospect. Introducing …
to solve optimization and control problems, which could impair its prospect. Introducing …
Fleet-dagger: Interactive robot fleet learning with scalable human supervision
Commercial and industrial deployments of robot fleets at Amazon, Nimble, Plus One,
Waymo, and Zoox query remote human teleoperators when robots are at risk or unable to …
Waymo, and Zoox query remote human teleoperators when robots are at risk or unable to …
Safety-aware human-in-the-loop reinforcement learning with shared control for autonomous driving
The learning from intervention (LfI) approach has been proven effective in improving the
performance of RL algorithms; nevertheless, existing methodologies in this domain tend to …
performance of RL algorithms; nevertheless, existing methodologies in this domain tend to …
Lazydagger: Reducing context switching in interactive imitation learning
R Hoque, A Balakrishna, C Putterman… - 2021 IEEE 17th …, 2021 - ieeexplore.ieee.org
Corrective interventions while a robot is learning to automate a task provide an intuitive
method for a human supervisor to assist the robot and convey information about desired …
method for a human supervisor to assist the robot and convey information about desired …
Human-in-the-loop deep reinforcement learning with application to autonomous driving
Due to the limited smartness and abilities of machine intelligence, currently autonomous
vehicles are still unable to handle all kinds of situations and completely replace drivers …
vehicles are still unable to handle all kinds of situations and completely replace drivers …
Safe decision-making for lane-change of autonomous vehicles via human demonstration-aided reinforcement learning
Decision-making is critical for lane change in autonomous driving. Reinforcement learning
(RL) algorithms aim to identify the values of behaviors in various situations and thus they …
(RL) algorithms aim to identify the values of behaviors in various situations and thus they …
Human Knowledge Enhanced Reinforcement Learning for Mandatory Lane-Change of Autonomous Vehicles in Congested Traffic
Mandatory lane-change scenarios are often challenging for autonomous vehicles in
complex environments. In this paper, a human-knowledge-enhanced reinforcement learning …
complex environments. In this paper, a human-knowledge-enhanced reinforcement learning …