Simulated Car Racing Championship: Competition Software Manual

By Daniele Loiacono, Luigi Cardamone, Pier Luca Lanzi. 2013

110 citations recorded by the source corpus.

This manual describes the competition software for the Simulated Car Racing Championship, an international competition held at major conferences in the field of Evolutionary Computation and in the field of Computational Intelligence and Games. It provides an overview of the architecture, the instructions to install the software and to run the simple drivers provided in the package, the description of the sensors and the actuators.

Subfields: Automotive Engineering

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In Automotive Engineering

Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “In practical applications, however, current artificial networks of spiking neurons cannot yet compete with the best traditional deep NNs (e.g., compare MNIST results of Section 5.19).”
Evolving large-scale neural networks for vision-based TORCS (2013, methodology): “In the standard setup for TORCS— used now for several years in reinforcement learning competitions [9, 10, 11]—a set of features describing the state of the car is provided to the driver.”
Synthetic Data for Deep Learning (2019, unknown): “TORCS has become the basis for the annual Simulated Car Racing Championship [375] and has been used in hundreds of works on autonomous driving and control systems (see Section 3.”
Continuous control for robot based on deep reinforcement learning (2019, methodology): “We train the autonomous vehicle in the open racing car simulator (TORCS) [71] which can provide a vivid 3D visualization, a complicated physics engine, and accurate vehicle dynamics which takes into consideration the traction, aerodynamics, fuel consumption and so on.”
Online Meta-Critic Learning for Off-Policy Actor-Critic Methods (2020, methodology): “The hyperparameters for TORCS can be found in the supplementary material.”
Autonomous vehicle control via deep reinforcement learning (2017, background): “Apart from Torcs, the CARMA project was inspired by DeepMind’s [2] progress with the Deep Q Network (DQN) algorithm in the ATARI environment.”