Search Results for author: Carl-Johan Hoel

Found 5 papers, 2 papers with code

Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous Driving

1 code implementation21 May 2021 Carl-Johan Hoel, Krister Wolff, Leo Laine

The distribution over returns is estimated by learning its quantile function implicitly, which gives the aleatoric uncertainty, whereas an ensemble of agents is trained on bootstrapped data to provide a Bayesian estimation of the epistemic uncertainty.

Autonomous Driving Decision Making +1

Reinforcement Learning with Uncertainty Estimation for Tactical Decision-Making in Intersections

no code implementations17 Jun 2020 Carl-Johan Hoel, Tommy Tram, Jonas Sjöberg

This paper investigates how a Bayesian reinforcement learning method can be used to create a tactical decision-making agent for autonomous driving in an intersection scenario, where the agent can estimate the confidence of its recommended actions.

Autonomous Driving Decision Making +1

Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation

1 code implementation22 Apr 2020 Carl-Johan Hoel, Krister Wolff, Leo Laine

This paper investigates how a Bayesian RL technique, based on an ensemble of neural networks with additional randomized prior functions (RPF), can be used to estimate the uncertainty of decisions in autonomous driving.

Autonomous Driving Decision Making +1

Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving

no code implementations6 May 2019 Carl-Johan Hoel, Katherine Driggs-Campbell, Krister Wolff, Leo Laine, Mykel J. Kochenderfer

This paper introduces a general framework for tactical decision making, which combines the concepts of planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning.

Autonomous Driving Decision Making +1

Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning

no code implementations14 Mar 2018 Carl-Johan Hoel, Krister Wolff, Leo Laine

This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function.

Decision Making reinforcement-learning

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