DotaService is a service to play Dota 2 through gRPC
DotaService is a service to play Dota 2 through gRPC. There are first class python bindings and examples, so you can play dota as you would use the OpenAI gym API.
It's fully functional and super lightweight. Starting Dota
obs = env.reset() takes 5 seconds,
obs = env.step(action) in the environment takes between 10 and 30 ms.
You can even set the config of
render=True and you can watch the game play live. Each game will
have a uuid and folder associated where there's a Dota demo (replay) and console logs.
Run DotaService Locally
Run the DotaService so you can connect your client to it later. Only one client per server is supported, and only one DotaService per VM (eg local or one per docker container).
python3 -m dotaservice >>> Serving on 127.0.0.1:13337
Run DotaService Distributed
To run two dockerservice instances, one on port
13337 and one on
13338, f.e. run:
docker run -dp 13337:13337 ds docker run -dp 13338:13337 ds
You can run as many as you want, until you run out of ports or ip addresses. If you are wearing your fancy pants, use Kubernetes to deploy gazillions.
from grpclib.client import Channel from protobuf.DotaService_grpc import DotaServiceStub from protobuf.DotaService_pb2 import Action from protobuf.DotaService_pb2 import Config # Connect to the DotaService. env = DotaServiceStub(Channel('127.0.0.1', 13337)) # Get the initial observation. observation = await env.reset(Config()) for i in range(8): # Sample an action from the action protobuf action = Action.MoveToLocation(x=.., y=.., z=..) # Take an action, returning the resulting observation. observation = await env.step(action)
This is very useful to provide an environment for reinforcement learning, and service aspect of it makes it
especially useful for distributed training. I am planning to provide a client python
module for this (
PyDota) that mimics typical OpenAI gym APIs. Maybe I won't even make PyDota
and the gRPC client is enough.
- Python 3.7
- Unix: MacOS, Ubuntu. A dockerfile is also provided see: docker/README.md.
Installing from pypi:
pip3 install dotaservice
For development; installing from source:
pip3 install -e .
(Optional) Compile the protos for Python (run from repository root):
python3 -m grpc_tools.protoc -I. --python_out=. --python_grpc_out=. dotaservice/protos/*.proto
From the benchmarks below you can derive that the dota service adds around 6±1 ms of time to
each action we take. Notice that Dota runs at a fixed (though not precise) 30 ticks/s.
When watching with
render=True it seems that the bot is running faster than realtime even at
host_timescale=1. And below (auto-generated) metrics show that it's running faster than real time
too. Q: what's going on?
My dev notes: NOTES.md.
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