Collision avoidance systems play a vital role in reducing the number of
vehicle accidents and saving human lives. This paper extends the previous work
using evolutionary neural networks for reactive collision avoidance. We are
proposing a new method we have called symmetric neural networks. The method
improves the model's performance by enforcing constraints between the network
weights which reduces the model optimization search space and hence, learns
more accurate control of the vehicle steering for improved maneuvering. The
training and validation processes are carried out using a simulation
environment - the codebase is publicly available. Extensive experiments are
conducted to analyze the proposed method and evaluate its performance. The
method is tested in several simulated driving scenarios. In addition, we have
analyzed the effect of the rangefinder sensor resolution and noise on the
overall goal of reactive collision avoidance. Finally, we have tested the
generalization of the proposed method. The results are encouraging; the
proposed method has improved the model's learning curve for training scenarios
and generalization to the new test scenarios. Using constrained weights has
significantly improved the number of generations required for the Genetic
Algorithm optimization.

本论文利用对称神经网络建立一种新的对抗性碰撞避免方法，通过在网络权重之间施加约束以减少模型优化搜索空间，提高车辆转向的控制精度，通过在模拟环境中进行训练和验证，分析了提出的方法并评估了其性能，结果表明该方法的学习曲线和一般化性良好。

使用对称性进化神经网络的无碰撞导航

Collision-Free Navigation using Evolutionary Symmetrical Neural Networks

Sub-optimal control policies in intersection traffic signal controllers (TSC)
contribute to congestion and lead to negative effects on human health and the
environment. Reinforcement learning (RL) for traffic signal control is a
promising approach to design better control policies and has attracted
considerable research interest in recent years. However, most work done in this
area used simplified simulation environments of traffic scenarios to train
RL-based TSC. To deploy RL in real-world traffic systems, the gap between
simplified simulation environments and real-world applications has to be
closed. Therefore, 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. In addition to the realistic simulation model, LemgoRL encompasses a
traffic signal logic unit that ensures compliance with all regulatory and
safety requirements. LemgoRL offers the same interface as the wellknown OpenAI
gym toolkit to enable easy deployment in existing research work. To demonstrate
the functionality and applicability of LemgoRL, we train a state-of-the-art
Deep RL algorithm on a CPU cluster utilizing a framework for distributed and
parallel RL and compare its performance with other methods. Our benchmark tool
drives the development of RL algorithms towards real-world applications.

本文介绍了一个名为 LemgoRL 的基准工具，为了关闭现有交通信号控制器在简化的模拟环境下不能正确地执行任务的差距，提出在更真实的模拟环境中进行 RL 算法的训练，以在真实世界的交通系统中应用。通过在这个基准工具上训练一个深度 RL 算法的性能表现，论文指出本基准工具推动 RL 算法向着真实世界应用方向的发展。

面向交通信号控制的强化学习实际应用

Towards Real-World Deployment of Reinforcement Learning for Traffic  Signal Control

We present iGibson 1.0, a novel simulation environment to develop robotic
solutions for interactive tasks in large-scale realistic scenes. Our
environment contains 15 fully interactive home-sized scenes with 108 rooms
populated with rigid and articulated objects. The scenes are replicas of
real-world homes, with distribution and the layout of objects aligned to those
of the real world. iGibson 1.0 integrates several key features to facilitate
the study of interactive tasks: i) generation of high-quality virtual sensor
signals (RGB, depth, segmentation, LiDAR, flow and so on), ii) domain
randomization to change the materials of the objects (both visual and physical)
and/or their shapes, iii) integrated sampling-based motion planners to generate
collision-free trajectories for robot bases and arms, and iv) intuitive
human-iGibson interface that enables efficient collection of human
demonstrations. Through experiments, we show that the full interactivity of the
scenes enables agents to learn useful visual representations that accelerate
the training of downstream manipulation tasks. We also show that iGibson 1.0
features enable the generalization of navigation agents, and that the
human-iGibson interface and integrated motion planners facilitate efficient
imitation learning of human demonstrated (mobile) manipulation behaviors.
iGibson 1.0 is open-source, equipped with comprehensive examples and
documentation. For more information, visit our project website:
this http URL

iGibson 1.0 是一个新型模拟环境，包含 15 个完全互动的具有 108 个房间的真实场景，为交互任务开发机器人解决方案提供了便利，同时集成了多个关键功能，包括动作规划和感官信号生成等，旨在为下游处理任务提供加速训练的可视化表示学习方案。