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Model. Comput. Simul."],"published-print":{"date-parts":[[2021,7,31]]},"abstract":"<jats:p>\n            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\n            <jats:sc>WiseMove<\/jats:sc>\n            can be transferred to our high-fidelity simulator, W\n            <jats:sc>ise<\/jats:sc>\n            M\n            <jats:sc>ove<\/jats:sc>\n            .\n            <jats:sc>WiseMove<\/jats:sc>\n            is a framework to study safety and other aspects of RL for autonomous driving. W\n            <jats:sc>ise<\/jats:sc>\n            M\n            <jats:sc>ove<\/jats:sc>\n            accurately reproduces the dynamics and software stack of our real vehicle.\n          <\/jats:p>\n          <jats:p>\n            We find that the accurately modelled perception errors in W\n            <jats:sc>ise<\/jats:sc>\n            M\n            <jats:sc>ove<\/jats:sc>\n            contribute the most to the transfer problem. These errors, when even naively modelled in\n            <jats:sc>WiseMove<\/jats:sc>\n            , provide an RL policy that performs better in W\n            <jats:sc>ise<\/jats:sc>\n            M\n            <jats:sc>ove<\/jats:sc>\n            than a hand-crafted rule-based policy. Applying domain randomization to the environment in\n            <jats:sc>WiseMove<\/jats:sc>\n            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.\n          <\/jats:p>","DOI":"10.1145\/3449356","type":"journal-article","created":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T16:04:06Z","timestamp":1626624246000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Transfer Reinforcement Learning for Autonomous Driving"],"prefix":"10.1145","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4841-438X","authenticated-orcid":false,"given":"Aravind","family":"Balakrishnan","sequence":"first","affiliation":[{"name":"University of Waterloo, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4390-7676","authenticated-orcid":false,"given":"Jaeyoung","family":"Lee","sequence":"additional","affiliation":[{"name":"University of Waterloo, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3071-9385","authenticated-orcid":false,"given":"Ashish","family":"Gaurav","sequence":"additional","affiliation":[{"name":"University of Waterloo, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1642-1101","authenticated-orcid":false,"given":"Krzysztof","family":"Czarnecki","sequence":"additional","affiliation":[{"name":"University of Waterloo, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2903-0823","authenticated-orcid":false,"given":"Sean","family":"Sedwards","sequence":"additional","affiliation":[{"name":"University of Waterloo, Canada"}]}],"member":"320","published-online":{"date-parts":[[2021,7,18]]},"reference":[{"unstructured":"Marcin Andrychowicz et\u00a0al. 2018. 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