Depreciated in faviour of my much faster implementation in Julia

Evolutionary strategies (deep neuroevolution) in pytorch using MPI

This implementation was made to be as simple and efficient as possible.
Reference implementation can be found here (in tensorflow using redis).
Based on two papers by uber AI labs here and here.

Implementation

This was made for use on a cluster using MPI (however it can be used on a single machine). With regards to efficiency it only scatters the positive fitness, negative fitness and noise index, per policy evaluated, to all other processes each generation. The noise is placed in a block of shared memory (on each node) for fast access and low memory footprint.

How to run

• conda install: conda install -n es_env -f env.yml
• example usages: simple_example.py obj.py nsra.py
• example configs are in config/

conda activate es_env
mpirun -np {num_procs} python simple_example.py configs/simple_conf.json

Make sure that you insert this line before you create your neural network as the initial creation sets the initial parameters, which must be deterministic across all threads

torch.random.manual_seed({seed})

General info

• In order to define a policy create a src.nn.nn.BaseNet (which is a simple extension of a torch.nn.Module) and pass it to a Policy along with an src.nn.optimizers.Optimizer and float value for the noise standard deviation, an example of this can be seen in simple_example.py.
• If you wish to share the noise using shared memory and MPI, then instantiate the NoiseTable using NoiseTable.create_shared(...), otherwise if you wish to use your own method of sharing noise/running sequentially then simply create the noise table using its constructor and pass your noise to it like this: NoiseTable(my_noise, n_params)
• NoiseTable.create_shared(...) will throw an error if less than 2 MPI procs are used