Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving
… a car-following model for autonomous velocity control based on reinforcement learning (RL)…
This model directly optimizes driving safety, efficiency, and comfort, by learning from …
This model directly optimizes driving safety, efficiency, and comfort, by learning from …
Data-efficient reinforcement learning with self-predictive representations
… We show that data augmentation can be more effectively leveraged in reinforcement
learning by forcing representations to be consistent between different augmented views of an …
learning by forcing representations to be consistent between different augmented views of an …
Deep reinforcement learning
SE Li - Reinforcement learning for sequential decision and …, 2023 - Springer
… in DRL and is often accompanied by severely low efficiency. One common strategy to eliminate
… We use the following update rule to demonstrate how the overestimation issue occurs: …
… We use the following update rule to demonstrate how the overestimation issue occurs: …
Human-level performance in 3D multiplayer games with population-based reinforcement learning
… sparse to be effectively used as the … through training. Opponent base camping (red) is
discovered early on, whereas teammate following (blue) becomes very prominent midway through …
discovered early on, whereas teammate following (blue) becomes very prominent midway through …
Training a helpful and harmless assistant with reinforcement learning from human feedback
… a weekly cadence with fresh human feedback data, efficiently improving our datasets and …
improving general-purpose instruction following). We also find that mixing preference model …
improving general-purpose instruction following). We also find that mixing preference model …
Advanced planning for autonomous vehicles using reinforcement learning and deep inverse reinforcement learning
… the same time, increase fuel efficiency and reduce congestion. … autonomous vehicle using
reinforcement learning techniques. … following, we formulate the inverse reinforcement learning …
reinforcement learning techniques. … following, we formulate the inverse reinforcement learning …
Crowd-robot interaction: Crowd-aware robot navigation with attention-based deep reinforcement learning
… Our model captures the Human-Human interactions occurring in dense crowds that … following
sections, we will present a novel Crowd-Robot Interaction model that can effectively learn …
sections, we will present a novel Crowd-Robot Interaction model that can effectively learn …
Dynamical hyperparameter optimization via deep reinforcement learning in tracking
… To overcome this challenge, we introduce an efficient heuristic strategy to handle high
dimensional state space, while also accelerating the convergence behavior. The proposed …
dimensional state space, while also accelerating the convergence behavior. The proposed …
Residual reinforcement learning for robot control
… and the computed torque method, usually follow predefined trajectories with little adaptive
… can solve tasks such as path following efficiently, applications that involve contacts between …
… can solve tasks such as path following efficiently, applications that involve contacts between …
Survey of deep reinforcement learning for motion planning of autonomous vehicles
S Aradi - IEEE Transactions on Intelligent Transportation …, 2020 - ieeexplore.ieee.org
… usually use DRL to define splines as a result of the training [… lanekeeping, trajectory following,
or car following is the higher… deep reinforcement learning techniques could be effectively …
or car following is the higher… deep reinforcement learning techniques could be effectively …
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