"Symphony: Learning Realistic and Diverse Agents for Autonomous Driving ..."

Maximilian Igl et al. (2022)

> Home

Details and statistics

DOI: 10.48550/ARXIV.2205.03195

access: open

type: Informal or Other Publication

metadata version: 2022-05-11

- view
  - electronic edition via DOI (open access)
  authority control:
- export record
  dblp key:
  - journals/corr/abs-2205-03195
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/journals/corr/abs-2205-03195
Maximilian Igl, Daewoo Kim, Alex Kuefler, Paul Mougin, Punit Shah, Kyriacos Shiarlis, Dragomir Anguelov, Mark Palatucci, Brandyn White, Shimon Whiteson:
Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation. CoRR abs/2205.03195 (2022)

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.