Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving

04/15/2019
by   Maxime Bouton, et al.
0

Designing reliable decision strategies for autonomous urban driving is challenging. Reinforcement learning (RL) has been used to automatically derive suitable behavior in uncertain environments, but it does not provide any guarantee on the performance of the resulting policy. We propose a generic approach to enforce probabilistic guarantees on an RL agent. An exploration strategy is derived prior to training that constrains the agent to choose among actions that satisfy a desired probabilistic specification expressed with linear temporal logic (LTL). Reducing the search space to policies satisfying the LTL formula helps training and simplifies reward design. This paper outlines a case study of an intersection scenario involving multiple traffic participants. The resulting policy outperforms a rule-based heuristic approach in terms of efficiency while exhibiting strong guarantees on safety.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

04/25/2019

Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments

Navigating urban environments represents a complex task for automated ve...
04/22/2020

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

Reinforcement learning (RL) can be used to create a tactical decision-ma...
04/11/2022

Automatically Learning Fallback Strategies with Model-Free Reinforcement Learning in Safety-Critical Driving Scenarios

When learning to behave in a stochastic environment where safety is crit...
08/29/2017

Safe Reinforcement Learning via Shielding

Reinforcement learning algorithms discover policies that maximize reward...
09/29/2020

Reannealing of Decaying Exploration Based On Heuristic Measure in Deep Q-Network

Existing exploration strategies in reinforcement learning (RL) often eit...
03/29/2019

Autonomous Highway Driving using Deep Reinforcement Learning

The operational space of an autonomous vehicle (AV) can be diverse and v...
11/22/2021

Bridging the gap between learning and heuristic based pushing policies

Non-prehensile pushing actions have the potential to singulate a target ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.