scholarly journals Dynamic Input for Deep Reinforcement Learning in Autonomous Driving

2019 ◽  
Author(s):  
Maria Huegle ◽  
Gabriel Kalweit ◽  
Branka Mirchevska ◽  
Moritz Werling ◽  
Joschka Boedecker
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Driving

Transfer Reinforcement Learning for Autonomous Driving

2021 ◽  
Vol 31 (3) ◽  
pp. 1-26
Author(s):  
Aravind Balakrishnan ◽  
Jaeyoung Lee ◽  
Ashish Gaurav ◽  
Krzysztof Czarnecki ◽  
Sean Sedwards
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Transfer Problem ◽  
Autonomous Driving ◽  
High Fidelity ◽  
Rule Based ◽  
High Level ◽  
Real Vehicle

Reinforcement learning (RL) is an attractive way to implement high-level decision-making policies for autonomous driving, but learning directly from a real vehicle or a high-fidelity simulator is variously infeasible. We therefore consider the problem of transfer reinforcement learning and study how a policy learned in a simple environment using WiseMove can be transferred to our high-fidelity simulator, W ise M ove . WiseMove is a framework to study safety and other aspects of RL for autonomous driving. W ise M ove accurately reproduces the dynamics and software stack of our real vehicle. We find that the accurately modelled perception errors in W ise M ove contribute the most to the transfer problem. These errors, when even naively modelled in WiseMove , provide an RL policy that performs better in W ise M ove than a hand-crafted rule-based policy. Applying domain randomization to the environment in WiseMove yields an even better policy. The final RL policy reduces the failures due to perception errors from 10% to 2.75%. We also observe that the RL policy has significantly less reliance on velocity compared to the rule-based policy, having learned that its measurement is unreliable.

Towards Autonomous Driving Decision by Combining Self-attention and Deep Reinforcement Learning

2021 ◽  
Author(s):  
Meiling Chen ◽  
Yanjie Li ◽  
Qi Liu ◽  
Shaohua Lv ◽  
Yunhong Xu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Driving
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