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5473393e30
Implement a simple HTTP server to start games, receive the gamestate and pass commands to the simulation. This is mainly intended for training reinforcement learning agents in 0 AD. As such, a python client and a small example are included. This option can be enabled using the -rl-interface flag. Patch by: irishninja Reviewed By: wraitii, Itms Fixes #5548 Differential Revision: https://code.wildfiregames.com/D2199 This was SVN commit r23917.
50 lines
1.4 KiB
Markdown
50 lines
1.4 KiB
Markdown
# 0 AD Python Client
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This directory contains `zero_ad`, a python client for 0 AD which enables users to control the environment headlessly.
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## Installation
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`zero_ad` can be installed with `pip` by running the following from the current directory:
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```
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pip install .
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```
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Development dependencies can be installed with `pip install -r requirements-dev.txt`. Tests are using pytest and can be run with `python -m pytest`.
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## Basic Usage
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If there is not a running instance of 0 AD, first start 0 AD with the RL interface enabled:
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```
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pyrogenesis --rl-interface=127.0.0.1:6000
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```
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Next, the python client can be connected with:
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```
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import zero_ad
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from zero_ad import ZeroAD
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game = ZeroAD('http://localhost:6000')
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```
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A map can be loaded with:
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```
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with open('./samples/arcadia.json', 'r') as f:
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arcadia_config = f.read()
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state = game.reset(arcadia_config)
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```
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where `./samples/arcadia.json` is the path to a game configuration JSON (included in the first line of the commands.txt file in a game replay directory) and `state` contains the initial game state for the given map. The game engine can be stepped (optionally applying actions at each step) with:
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```
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state = game.step()
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```
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For example, enemy units could be attacked with:
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```
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my_units = state.units(owner=1)
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enemy_units = state.units(owner=2)
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actions = [zero_ad.actions.attack(my_units, enemy_units[0])]
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state = game.step(actions)
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```
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For a more thorough example, check out samples/simple-example.py!
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