Purpose in the Machine: Do Traffic Simulators Produce Distributionally Equivalent Outcomes for Reinforcement Learning Applications?

• URL: http://arxiv.org/abs/2311.08429v1
• Date: Tue, 14 Nov 2023 01:05:14 GMT
• Title: Purpose in the Machine: Do Traffic Simulators Produce Distributionally Equivalent Outcomes for Reinforcement Learning Applications?
• Authors: Rex Chen, Kathleen M. Carley, Fei Fang, Norman Sadeh
• Abstract summary: This work focuses on two simulators commonly used to train reinforcement learning (RL) agents for traffic applications, CityFlow and SUMO. A controlled virtual experiment varying driver behavior and simulation scale finds evidence against distributional equivalence in RL-relevant measures from these simulators. While granular real-world validation generally remains infeasible, these findings suggest that traffic simulators are not a deus ex machina for RL training.
• Score: 35.719833726363085
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traffic simulators are used to generate data for learning in intelligent transportation systems (ITSs). A key question is to what extent their modelling assumptions affect the capabilities of ITSs to adapt to various scenarios when deployed in the real world. This work focuses on two simulators commonly used to train reinforcement learning (RL) agents for traffic applications, CityFlow and SUMO. A controlled virtual experiment varying driver behavior and simulation scale finds evidence against distributional equivalence in RL-relevant measures from these simulators, with the root mean squared error and KL divergence being significantly greater than 0 for all assessed measures. While granular real-world validation generally remains infeasible, these findings suggest that traffic simulators are not a deus ex machina for RL training: understanding the impacts of inter-simulator differences is necessary to train and deploy RL-based ITSs.

Related papers

• Learning Realistic Traffic Agents in Closed-loop [36.38063449192355]
  Reinforcement learning (RL) can train traffic agents to avoid infractions, but using RL alone results in unhuman-like driving behaviors. We propose Reinforcing Traffic Rules (RTR) to match expert demonstrations under a traffic compliance constraint. Our experiments show that RTR learns more realistic and generalizable traffic simulation policies.
  arXiv  Detail & Related papers  (2023-11-02T16:55:23Z)
• Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop [1.8218298349840023]
  Reinforcement Learning has great potential for autonomously training agents to perform complex control tasks. To use RL effectively in embedded system function development, the generated agents must be able to handle real-world applications. This work focuses on accelerating the training process of RL agents by combining Transfer Learning (TL) and X-in-the-Loop (XiL) simulation.
  arXiv  Detail & Related papers  (2023-10-25T09:13:12Z)
• Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
  Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
  arXiv  Detail & Related papers  (2023-10-12T20:49:15Z)
• Rethinking Closed-loop Training for Autonomous Driving [82.61418945804544]
  We present the first empirical study which analyzes the effects of different training benchmark designs on the success of learning agents. We propose trajectory value learning (TRAVL), an RL-based driving agent that performs planning with multistep look-ahead. Our experiments show that TRAVL can learn much faster and produce safer maneuvers compared to all the baselines.
  arXiv  Detail & Related papers  (2023-06-27T17:58:39Z)
• A Platform-Agnostic Deep Reinforcement Learning Framework for Effective Sim2Real Transfer towards Autonomous Driving [0.0]
  Deep Reinforcement Learning (DRL) has shown remarkable success in solving complex tasks. transferring DRL agents to the real world is still challenging due to the significant discrepancies between simulation and reality. We propose a robust DRL framework that leverages platform-dependent perception modules to extract task-relevant information.
  arXiv  Detail & Related papers  (2023-04-14T07:55:07Z)
• LemgoRL: An open-source Benchmark Tool to Train Reinforcement Learning Agents for Traffic Signal Control in a real-world simulation scenario [0.0]
  Sub-optimal control policies in intersection traffic signal controllers (TSC) contribute to congestion and lead to negative effects on human health and the environment. We propose LemgoRL, a benchmark tool to train RL agents as TSC in a realistic simulation environment of Lemgo, a medium-sized town in Germany. LemgoRL offers the same interface as the well-known OpenAI gym toolkit to enable easy deployment in existing research work.
  arXiv  Detail & Related papers  (2021-03-30T10:11:09Z)
• TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [74.67698916175614]
  We propose TrafficSim, a multi-agent behavior model for realistic traffic simulation. In particular, we leverage an implicit latent variable model to parameterize a joint actor policy. We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
  arXiv  Detail & Related papers  (2021-01-17T00:29:30Z)
• RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real [74.45688231140689]
  We introduce the RL-scene consistency loss for image translation, which ensures that the translation operation is invariant with respect to the Q-values associated with the image. We obtain RL-CycleGAN, a new approach for simulation-to-real-world transfer for reinforcement learning.
  arXiv  Detail & Related papers  (2020-06-16T08:58:07Z)
• Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents [0.0]
  We develop a data driven traffic simulator by training a generative adverserial network (GAN) on real life trajectory data. The simulator generates randomized trajectories that resembles real life traffic interactions between vehicles. We demonstrate through simulations that RL agents trained on GAN-based traffic simulator has stronger generalization capabilities compared to RL agents trained on simple rule-driven simulators.
  arXiv  Detail & Related papers  (2020-06-10T13:14:34Z)
• From Simulation to Real World Maneuver Execution using Deep Reinforcement Learning [69.23334811890919]
  Deep Reinforcement Learning has proved to be able to solve many control tasks in different fields, but the behavior of these systems is not always as expected when deployed in real-world scenarios. This is mainly due to the lack of domain adaptation between simulated and real-world data together with the absence of distinction between train and test datasets. We present a system based on multiple environments in which agents are trained simultaneously, evaluating the behavior of the model in different scenarios.
  arXiv  Detail & Related papers  (2020-05-13T14:22:20Z)

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.