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dotaservice

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Commits

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733db265f04fa10caab57fa34f7f85861095dc2e

fixes to launching dotaservice using asyncio

NNostrademous committed 6 years ago
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9d5e82c1c161df75bfe92abc621cd2d5f50b4e1f

fixes to item pickup

NNostrademous committed 6 years ago
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da1323078823c463ad8980c34e85c2ea7c2e411a

added courier API

NNostrademous committed 6 years ago
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dfe882362c96b9979614abccf71fc9dcd6f8294b

Revert timeouts.

TTimZaman committed 6 years ago
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9a0e7a958ea861f6243e2ec7395c87d0838589bf

Double observe timeout

TTimZaman committed 6 years ago
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42ad77281af171b022fcfb10ee5fae2e773f0717

Reconfigure logger

TTimZaman committed 6 years ago

README

The README file for this repository.

DotaService

dotaservice icon


NOTE: The project that uses the dotaservice in a k8s environment is the DotaClient repo.

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, and each 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.

demo

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

See docker/README.md.

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.

Client Code

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.

dotaservice connections

Requirements

  • Python 3.7
  • Unix: MacOS, Ubuntu. A dockerfile is also provided see: docker/README.md.

Installation

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=. --grpc_python_out=. dotaservice/protos/*.proto

Notes

My dev notes: NOTES.md.


Acknowledgements