TORCS, The Open Racing Car Simulator, v1.3.5

By Bernhard Wymann, Christos Dimitrakakis, Andrew Sumner, E. Espié, Christophe Guionneau, Rémi Coulom. 2013

31 citations recorded by the source corpus.

Subfields: Reinforcement Learning, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Reinforcement Learning

1. Sergey Levine
2. Pieter Abbeel
3. Abhinav Kumar Gupta
4. Timothy P. Lillicrap
5. Chelsea Finn
6. Ali Farhadi
7. Shimon Whiteson
8. David Silver

In Evaluation & Benchmarks

1. Abhinav Kumar Gupta
2. Ali Farhadi
3. Li Fei-Fei
4. Roozbeh Mottaghi
5. Yuke Zhu
6. Alex Graves
7. Timothy P. Lillicrap
8. David Silver

Citations in the corpus

• Asynchronous Methods for Deep Reinforcement Learning (2016, methodology): “tep Q and n-step Q methods presented in this work with similar potential improvements. 5.2.2 TORCS Car Racing Simulator We also compared the four asynchronous methods on the TORCS 3D car racing game [Wymann et al., 2013]. TORCS not only has more realistic graphics than Atari 2600 games, but also req”
• Target-driven visual navigation in indoor scenes using deep reinforcement learning (2016, methodology): “Additionally, our method is generalizable to new scenes and new targets, while the mentioned methods should be re-trained for a new game, or in case of a change in the game rules.”