yanpanlau

3 ms·

1. 1.▲
   by yanpanlau 9y ago

   Just use linux
2. 2.▲
   by yanpanlau 10y ago

   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
3. 3.▲
   by yanpanlau 10y ago

   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?
4. 4.▲
   by yanpanlau 10y ago

   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.
5. 5.▲
   by yanpanlau 10y ago

   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 c
6. 6.▲
   by yanpanlau 10y ago

   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://gith
7. 7.▲
   DQN for Beginners in 200 lines of python code to play Flappy Bird with Keras(yanpanlau.github.io)

   5 points by yanpanlau 10y ago|1 comments