Batched pong environment

Instead of running one environment at a time or one per thread, run everything in batch using numpy on a single core.

@misc{batch_pong_poc,
  author       = {Andreas Kirsch},
  title        = {Batched pong environment},
  month        = feb,
  year         = 2018,
  doi          = {10.5281/zenodo.1175920},
  url          = {https://github.com/BlackHC/batch_pong_poc/tree/master}
}

Time per step per environment

Legend

• atari: using the gym's atari emulator
• numpy: batched Python implementation using numpy
• vanilla: simple Python implementation

Measurements against a single core with a single thread. The environment can be run 500x faster (compared to the Atari emulator), respectively 60x faster (compared to a vanilla Python implementation).

Trade-offs

Obviously, this is not equal to Atari Pong in any way but it is sufficient to model the behavior of the game. Similar batched environments could be implemented for Space Invaders, Catch or Sokoban. For initial experiments, such batched environments can provide higher throughputs while freeing compute resources for training and inference.

The code is harder to read because of how parallel operations are specified in numpy. It would be nice if there was a way to use CUDA's SIMT (single instruction, multiple threads) model in Python.

The biggest gains are realized for very big batch sizes. Since bigger batch sizes only reduce variance sublinearly, it would make sense to use this environment in conjunction with Evolutionary Algorithms or Population-based Training to train multiple models in parallel/lock-step.

Further research

Is there a way for the vanilla batch version to be vectorized automatically? This could work similar to CUDA's SIMT. Could the Atari emulator be updated to execute multiple simulations in lock-step as well. The entire state can be expressed in less than 1 KB of data, so many simulations could be run in a cache-efficient way.