21,342 research outputs found
A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing
Autonomous racing with scaled race cars has gained increasing attention as an
effective approach for developing perception, planning and control algorithms
for safe autonomous driving at the limits of the vehicle's handling. To train
agile control policies for autonomous racing, learning-based approaches largely
utilize reinforcement learning, albeit with mixed results. In this study, we
benchmark a variety of imitation learning policies for racing vehicles that are
applied directly or for bootstrapping reinforcement learning both in simulation
and on scaled real-world environments. We show that interactive imitation
learning techniques outperform traditional imitation learning methods and can
greatly improve the performance of reinforcement learning policies by
bootstrapping thanks to its better sample efficiency. Our benchmarks provide a
foundation for future research on autonomous racing using Imitation Learning
and Reinforcement Learning
Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning
Recent advances in combining deep learning and Reinforcement Learning have
shown a promising path for designing new control agents that can learn optimal
policies for challenging control tasks. These new methods address the main
limitations of conventional Reinforcement Learning methods such as customized
feature engineering and small action/state space dimension requirements. In
this paper, we leverage one of the state-of-the-art Reinforcement Learning
methods, known as Trust Region Policy Optimization, to tackle intersection
management for autonomous vehicles. We show that using this method, we can
perform fine-grained acceleration control of autonomous vehicles in a grid
street plan to achieve a global design objective.Comment: Accepted in IEEE Smart World Congress 201
Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning
Providing an efficient strategy to navigate safely through unsignaled
intersections is a difficult task that requires determining the intent of other
drivers. We explore the effectiveness of Deep Reinforcement Learning to handle
intersection problems. Using recent advances in Deep RL, we are able to learn
policies that surpass the performance of a commonly-used heuristic approach in
several metrics including task completion time and goal success rate and have
limited ability to generalize. We then explore a system's ability to learn
active sensing behaviors to enable navigating safely in the case of occlusions.
Our analysis, provides insight into the intersection handling problem, the
solutions learned by the network point out several shortcomings of current
rule-based methods, and the failures of our current deep reinforcement learning
system point to future research directions.Comment: IEEE International Conference on Robotics and Automation (ICRA 2018
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