Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing

• URL: http://arxiv.org/abs/2312.06406v2
• Date: Mon, 5 Aug 2024 17:00:00 GMT
• Title: Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing
• Authors: Andrew Murdoch, Johannes Cornelius Schoeman, Hendrik Willem Jordaan,
• Abstract summary: This paper addresses the issue of increasing the performance of reinforcement learning (RL) solutions for autonomous racing cars. We propose a partial end-to-end algorithm that decouples the planning and control tasks. By leveraging the robustness of a classical controller, our partial end-to-end driving algorithm exhibits better robustness towards model mismatches than standard end-to-end algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we address the issue of increasing the performance of reinforcement learning (RL) solutions for autonomous racing cars when navigating under conditions where practical vehicle modelling errors (commonly known as \emph{model mismatches}) are present. To address this challenge, we propose a partial end-to-end algorithm that decouples the planning and control tasks. Within this framework, an RL agent generates a trajectory comprising a path and velocity, which is subsequently tracked using a pure pursuit steering controller and a proportional velocity controller, respectively. In contrast, many current learning-based (i.e., reinforcement and imitation learning) algorithms utilise an end-to-end approach whereby a deep neural network directly maps from sensor data to control commands. By leveraging the robustness of a classical controller, our partial end-to-end driving algorithm exhibits better robustness towards model mismatches than standard end-to-end algorithms.

Related papers

• Autonomous Vehicle Controllers From End-to-End Differentiable Simulation [60.05963742334746]
  We propose a differentiable simulator and design an analytic policy gradients (APG) approach to training AV controllers. Our proposed framework brings the differentiable simulator into an end-to-end training loop, where gradients of environment dynamics serve as a useful prior to help the agent learn a more grounded policy. We find significant improvements in performance and robustness to noise in the dynamics, as well as overall more intuitive human-like handling.
  arXiv  Detail & Related papers  (2024-09-12T11:50:06Z)
• Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch [72.26822499434446]
  Auto-Train-Once (ATO) is an innovative network pruning algorithm designed to automatically reduce the computational and storage costs of DNNs. We provide a comprehensive convergence analysis as well as extensive experiments, and the results show that our approach achieves state-of-the-art performance across various model architectures.
  arXiv  Detail & Related papers  (2024-03-21T02:33:37Z)
• DTC: Deep Tracking Control [16.2850135844455]
  We propose a hybrid control architecture that combines the advantages of both worlds to achieve greater robustness, foot-placement accuracy, and terrain generalization. A deep neural network policy is trained in simulation, aiming to track the optimized footholds. We demonstrate superior robustness in the presence of slippery or deformable ground when compared to model-based counterparts.
  arXiv  Detail & Related papers  (2023-09-27T07:57:37Z)
• FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing [71.76084256567599]
  We present a system that enables an autonomous small-scale RC car to drive aggressively from visual observations using reinforcement learning (RL) Our system, FastRLAP (faster lap), trains autonomously in the real world, without human interventions, and without requiring any simulation or expert demonstrations. The resulting policies exhibit emergent aggressive driving skills, such as timing braking and acceleration around turns and avoiding areas which impede the robot's motion, approaching the performance of a human driver using a similar first-person interface over the course of training.
  arXiv  Detail & Related papers  (2023-04-19T17:33:47Z)
• Tackling Real-World Autonomous Driving using Deep Reinforcement Learning [63.3756530844707]
  In this work, we propose a model-free Deep Reinforcement Learning Planner training a neural network that predicts acceleration and steering angle. In order to deploy the system on board the real self-driving car, we also develop a module represented by a tiny neural network.
  arXiv  Detail & Related papers  (2022-07-05T16:33:20Z)
• Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning [13.699336307578488]
  Deep imitative reinforcement learning approach (DIRL) achieves agile autonomous racing using visual inputs. We validate our algorithm both in a high-fidelity driving simulation and on a real-world 1/20-scale RC-car with limited onboard computation.
  arXiv  Detail & Related papers  (2021-07-18T00:00:48Z)
• Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
  We present an efficient and robust LiDAR-based end-to-end navigation framework. We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
  arXiv  Detail & Related papers  (2021-05-20T17:52:37Z)
• Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning [52.2663102239029]
  We present a new practical framework based on deep reinforcement learning and decision-time planning for real-world vehicle on idle-hailing platforms. Our approach learns ride-based state-value function using a batch training algorithm with deep value. We benchmark our algorithm with baselines in a ride-hailing simulation environment to demonstrate its superiority in improving income efficiency.
  arXiv  Detail & Related papers  (2021-03-08T05:34:05Z)
• Collision-Free Flocking with a Dynamic Squad of Fixed-Wing UAVs Using Deep Reinforcement Learning [2.555094847583209]
  We deal with the decentralized leader-follower flocking control problem through deep reinforcement learning (DRL) We propose a novel reinforcement learning algorithm CACER-II for training a shared control policy for all the followers. As a result, the variable-length system state can be encoded into a fixed-length embedding vector, which makes the learned DRL policies independent with the number or the order of followers.
  arXiv  Detail & Related papers  (2021-01-20T11:23:35Z)
• An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space [0.0]
  This paper presents a novel end-to-end continuous deep reinforcement learning approach towards autonomous cars' decision-making and motion planning. For the first time, we define both states and action spaces on the Frenet space to make the driving behavior less variant to the road curvatures. The algorithm generates continuoustemporal trajectories on the Frenet frame for the feedback controller to track.
  arXiv  Detail & Related papers  (2020-11-26T02:40:07Z)
• Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning [21.500697097095408]
  Planning safe trajectories under uncertain and dynamic conditions makes the autonomous driving problem significantly complex. Current sampling-based methods such as Rapidly Exploring Random Trees (RRTs) are not ideal for this problem because of the high computational cost. We propose a Hierarchical Reinforcement Learning structure combined with a Proportional-Integral-Derivative (PID) controller for trajectory planning.
  arXiv  Detail & Related papers  (2020-11-09T20:49:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.