yanpanlau

Just use linux

Please find the result below. I modified the reward function such that staying in the middle of the track is no longer required.

https://youtu.be/Tb5gASEJIRM

Hi Bluetwo, I am currently travelling to the San Francisco right now. Can you send me a e-mail yanpan@gmail.com so I can contact you and e-mail you the result directly when I back to Hong Kong?

Hi~I used Aalborg track as my training dataset and I used Alpine1 track as my validation dataset. The Alpine1 track is 3 times longer than Aalborg. As you can see on the video, the agent can drive reasonably OK on the validation dataset.

Staying in the middle of the track is not a necessary requirement in the reward function. The reason I include it is to speed up the learning time in the beginning. You can remove it once you learn a reasonable policy and see it the agent can find the optimal apex path. I will do a test tonight.

Just like in human world : You first learn how to drive before you learn how to drift the car.

It is quite easy to change the input features as pixels and fit into convnet under Keras (That's why I love Keras so much). However, gym_torcs only support 64x64 pixels and it is hard to see by human eyes, IMHO.

https://github.com/ugo-nama-kun/gym_torcs/issues/4