Improving Environment Robustness of Deep Reinforcement Learning
  Approaches for Autonomous Racing Using Bayesian Optimization-based Curriculum
  Learning
Rohan Banerjee Prishita Ray Mark Campbell
Deep reinforcement learning (RL) approaches have been broadly applied to a
large number of robotics tasks, such as robot manipulation and autonomous
driving. However, an open problem in deep RL is learning policies that are
robust to variations in the environment, which is an important condition for
such systems to be deployed into real-world, unstructured settings. Curriculum
learning is one approach that has been applied to improve generalization
performance in both supervised and reinforcement learning domains, but
selecting the appropriate curriculum to achieve robustness can be a
user-intensive process. In our work, we show that performing probabilistic
inference of the underlying curriculum-reward function using Bayesian
Optimization can be a promising technique for finding a robust curriculum. We
demonstrate that a curriculum found with Bayesian optimization can outperform a
vanilla deep RL agent and a hand-engineered curriculum in the domain of
autonomous racing with obstacle avoidance. Our code is available at
https://github.com/PRISHIta123/Curriculum_RL_for_Driving.