title: CaRL: Learning Scalable Planning Policies with Simple Rewards
summary: We investigate reinforcement learning (RL) for privileged planning in
  autonomous driving. State-of-the-art approaches for this task are rule-based,
  but these methods do not scale to the long tail. RL, on the other hand, is
  scalable and does not suffer from compounding errors like imitation learning.
  Contemporary RL approaches for driving use complex shaped rewards that sum
  multiple individual rewards, \eg~progress, position, or orientation rewards. We
  show that PPO fails to optimize a popular version of these rewards when the
  mini-batch size is increased, which limits the scalability of these approaches.
  Instead, we propose a new reward design based primarily on optimizing a single
  intuitive reward term: route completion. Infractions are penalized by
  terminating the episode or multiplicatively reducing route completion. We find
  that PPO scales well with higher mini-batch sizes when trained with our simple
  reward, even improving performance. Training with large mini-batch sizes
  enables efficient scaling via distributed data parallelism. We scale PPO to
  300M samples in CARLA and 500M samples in nuPlan with a single 8-GPU node. The
  resulting model achieves 64 DS on the CARLA longest6 v2 benchmark,
  outperforming other RL methods with more complex rewards by a large margin.
  Requiring only minimal adaptations from its use in CARLA, the same method is
  the best learning-based approach on nuPlan. It scores 91.3 in non-reactive and
  90.6 in reactive traffic on the Val14 benchmark while being an order of
  magnitude faster than prior work.
paper_authors._id: 68126aa5ec67f8f30bf43f20
paper_authors.name: Bernhard Jaeger
paper_authors._id: 68126aa5ec67f8f30bf43f21
paper_authors.name: Daniel Dauner
paper_authors._id: 68126aa5ec67f8f30bf43f22
paper_authors.name: Jens Beißwenger
paper_authors._id: 68126aa5ec67f8f30bf43f23
paper_authors.name: Simon Gerstenecker
paper_authors._id: 68126aa5ec67f8f30bf43f24
paper_authors.name: Kashyap Chitta
paper_authors._id: 68126aa5ec67f8f30bf43f25
paper_authors.name: Andreas Geiger
paper_title: CaRL: Learning Scalable Planning Policies with Simple Rewards
paper_summary: We investigate reinforcement learning (RL) for privileged planning in
  autonomous driving. State-of-the-art approaches for this task are rule-based,
  but these methods do not scale to the long tail. RL, on the other hand, is
  scalable and does not suffer from compounding errors like imitation learning.
  Contemporary RL approaches for driving use complex shaped rewards that sum
  multiple individual rewards, \eg~progress, position, or orientation rewards. We
  show that PPO fails to optimize a popular version of these rewards when the
  mini-batch size is increased, which limits the scalability of these approaches.
  Instead, we propose a new reward design based primarily on optimizing a single
  intuitive reward term: route completion. Infractions are penalized by
  terminating the episode or multiplicatively reducing route completion. We find
  that PPO scales well with higher mini-batch sizes when trained with our simple
  reward, even improving performance. Training with large mini-batch sizes
  enables efficient scaling via distributed data parallelism. We scale PPO to
  300M samples in CARLA and 500M samples in nuPlan with a single 8-GPU node. The
  resulting model achieves 64 DS on the CARLA longest6 v2 benchmark,
  outperforming other RL methods with more complex rewards by a large margin.
  Requiring only minimal adaptations from its use in CARLA, the same method is
  the best learning-based approach on nuPlan. It scores 91.3 in non-reactive and
  90.6 in reactive traffic on the Val14 benchmark while being an order of
  magnitude faster than prior work.
paper_ai_summary: Reinforcement learning with a simplified reward design achieves superior performance in autonomous driving tasks by scaling well with larger mini-batch sizes and distributed training.
paper_ai_keywords: reinforcement learning privileged planning RL PPO mini-batch size distributed data parallelism CARLA nuPlan Val14 benchmark