issues with gym about practical_rl HOT 4 CLOSED

Contributed by Sergey Kolesnikov:

no GLX on GPU nodes

GLXInfoException: pyglet requires an X server with GLX
Solution: re-install GPU drivers without opengl.
openai/gym#366
https://davidsanwald.github.io/2016/11/13/building-tensorflow-with-gpu-support.html

from practical_rl.

Game ends in 200 ticks

The current newest version of gym force-stops environment in 200 steps even if you don't use env.monitor.
This may ruin CEM on MountainCar and others.(week1 homework)
To avoid this, use env = gym.make("MountainCar-v0").env

from practical_rl.

For the bonus part of week 0 homework, what is the expected time to solve "taxi-v1" with a genetic algorithm? The problem is that game ends in 200 ticks and in the end all policies in the pool are stuck at -200. Then I tried not to monitor the is_done flag and tried to monitor when the reward is 20 instead. I found that it would probably take several days to get a score of -100 on my notebook (CPU=i5). So I interrupted the process. I just want to check is it normal to take such a long time?

Below is what I got in 8 hours (I set t_max = 11000).
Epoch 0:
best score: -44656.76
Epoch 1:
best score: -42678.38
Epoch 2:
best score: -42678.56
Epoch 3:
best score: -32778.74
Epoch 4:
best score: -32778.74
Epoch 5:
best score: -44657.84
Epoch 6:
best score: -42678.56
Epoch 7:
best score: -38718.74
Epoch 8:
best score: -38717.12
Epoch 9:
best score: -38718.74
Epoch 10:
best score: -36738.02
Epoch 11:
best score: -32777.84
Epoch 12:
best score: -38717.12
Epoch 13:
best score: -36738.02
Epoch 14:
best score: -32779.1
Epoch 15:
best score: -28819.28
Epoch 16:
best score: -30799.28
Epoch 17:
best score: -30799.64
Epoch 18:
best score: -32779.28
Epoch 19:
best score: -30800.0
Epoch 20:
best score: -32779.1
Epoch 21:
best score: -30799.1
Epoch 22:
best score: -32777.48
Epoch 23:
best score: -32778.38
Epoch 24:
best score: -30799.64

from practical_rl.

For the record, we fixed it by removing time limit (env = gym.make('Taxi-v1').env)

from practical_rl.