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ppg-pong

Using Parallel Policy Gradients with MPI and few dependencies to learn to play Atari Pong. It's really fast and despite being a ridiculously simple neural network it learns to defeat Pong in less than 5 minutes (on 1536 cores).

This simple implementation is based on Andrej Karpathy's excellent example code: https://gist.github.com/karpathy/a4166c7fe253700972fcbc77e4ea32c5

You can read more details in the blog post here: https://www.allinea.com/blog/201610/deep-learning-episode-3-supercomputer-vs-pong

Dependencies

  • Numpy
  • mpi4py
  • OpenAI Gym

Example

$ mpirun -n 4 python ppg-pong.py
[2016-12-07 08:28:50,733] Making new env: Pong-v0
[2016-12-07 08:28:50,735] Making new env: Pong-v0
[2016-12-07 08:28:50,735] Making new env: Pong-v0
[2016-12-07 08:28:50,736] Making new env: Pong-v0
Running with 4 processes
     2, 0000002675 steps @   1054/s: -10.8 | -10.7500 @ 1.0e-03 lr, 10750.0 r/Kup
     4, 0000005207 steps @   1047/s: -10.2 | -10.7250 @ 1.0e-03 lr,   500.0 r/Kup
     7, 0000007745 steps @   1045/s: -10.0 | -10.6887 @ 1.0e-03 lr,   625.0 r/Kup
     9, 0000010270 steps @   1043/s:  -6.8 | -10.4918 @ 1.0e-03 lr,  1062.5 r/Kup
    12, 0000012810 steps @   1043/s: -12.0 | -10.5672 @ 1.0e-03 lr,   458.3 r/Kup
    14, 0000015342 steps @   1040/s: -11.0 | -10.5889 @ 1.0e-03 lr,   138.9 r/Kup
    17, 0000017842 steps @   1038/s: -10.0 | -10.5594 @ 1.0e-03 lr,   131.9 r/Kup
    19, 0000020388 steps @   1040/s: -11.2 | -10.5939 @ 1.0e-03 lr,  -406.2 r/Kup
    22, 0000022907 steps @   1040/s: -10.8 | -10.6017 @ 1.0e-03 lr,  -390.6 r/Kup
    24, 0000025438 steps @   1040/s:  -8.2 | -10.4842 @ 1.0e-03 lr,   -60.0 r/Kup
    26, 0000027992 steps @   1040/s:  -9.2 | -10.4225 @ 1.0e-03 lr,   -26.7 r/Kup
    ...
    92, 0000096483 steps @   1046/s: -10.8 |  -9.6679 @ 1.0e-03 lr,    27.0 r/Kup
    94, 0000099001 steps @   1046/s: -10.0 |  -9.6845 @ 1.0e-03 lr,    25.3 r/Kup
    97, 0000101520 steps @   1045/s:  -9.2 |  -9.6628 @ 1.0e-03 lr,    28.8 r/Kup
    99, 0000104046 steps @   1045/s:  -9.8 |  -9.6671 @ 1.0e-03 lr,    27.7 r/Kup
   101, 0000106581 steps @   1045/s:  -8.2 |  -9.5963 @ 1.0e-03 lr,    28.3 r/Kup
   104, 0000109107 steps @   1045/s:  -7.5 |  -9.4915 @ 1.0e-03 lr,    32.2 r/Kup
   106, 0000111613 steps @   1045/s:  -7.0 |  -9.3669 @ 1.0e-03 lr,    32.0 r/Kup
   109, 0000114130 steps @   1044/s: -10.5 |  -9.4235 @ 1.0e-03 lr,    29.3 r/Kup

After training for 3.9 minutes on 1536 cores this agent learns to defeat Atari Pong (reward >0):

![Training reward over time at 1536 cores](Training chart at 1536 cores.png)

Note that the training reward is averaged over 2 second windows (see the blog post for more details). The test reward when played out over an entire game (first to 21 points) continues to increase with additional training time.

Video

An agent trained for 28 minutes on 1536 cores looks like this.