This is a local, not distributed, go, not python, implementation of the Evolution Strategies as a Scalable Alternative to Reinforcement Learning (Salimans et. al). The original starter from the paper can be found openai/evolution-strategies-starter. Under the covers it uses the openai/gym-http-api, more specifically binding-go, and uses unixpickle/anynet and unixpickle/anyvec for efficient high-level vector computation. Enjoy!

The goal is to solve CartPole-v0, This requires 195 epochs/reward over 100 episodes. Install openai/gym, openai/gym-http-api is a dependency required from the Go source.

Get the binary. Clone, download, or whatever you want, or just

$ go get github.com/wenkesj/evolution

In a seperate terminal, open the gym from wherever github.com/openai/gym-http-api is located in your fs.

$ python gym_http_server.py

Run the trainer and evaluater with whatever concauction you choose.

$ # 200 episodes of "training" by 2 agents and 100
$ # finalepisodes of evaluation with a single agent
$ # Saving results to a directory "~/agents2eps200"
$ evolution --outmonitor ~/agents2eps200 \
  --finalepisodes 100 \
  --episodes 200
  --agents 2

So, after 42 episodes, the 2 agents evolve enough to simply destroy at the game on their own. In this simple case, we apply a cutoff average reward of 195 or above for both agents, signifying the parameters on average should be able to solve the game with a single offspring. So we test that fact,

And it works! We get 198.5 average reward over 100 episodes!

• Parallelize where needed to avoid embarrassment 😏
• 32/64 bit support?
• Support multiple environments gym-http-api#47
• Serialize/deserialize networks the anynet way
  • Goals $ evolution -net net.proto -env Pong-v0 ...
  • Input network for specific environment (i.e. Pong-v0)
  • Save/load
• Optimizations
  • Adam (ADAM!!!!! 😉), SGD variable?
  • unixpickle/cudavec for gigs
• Plotting, statistics, performance profiling, uploading

This is a project for my Complex Systems and Networks class. This isn't meant to be comparable to the original work; I'm not a master coder/statistical god/andrej karpathy, I just thought this was a cool idea. This is an implementation with results and intrepretation.