Lux AI 2021 python game engine and gym
This is a replica of the Lux AI 2021 game ported directly over to python. It also sets up a classic Reinforcement Learning gym environment to be used to train RL agents for creating agents.
| Features | LuxAi2021 |
|---|---|
| Lux game engine porting to python |
|
| Documentation |
|
| All actions supported |
|
| PPO example training agent |
|
| Example agent converges to a good policy |
|
| Kaggle submission format agents |
|
| Lux replay viewer support |
|
| Game engine consistency validation to base game |
|
Installation
This should work cross-platform, but I've only tested Windows 10 and Ubuntu.
Important: Use Python 3.7.* for training your models. This is required since when you create a Kaggle submission, the Kaggle competition will run the code using Python 3.7.*, and you will get a model deserialization error if you train the model with Python 3.8>=.
Install luxai2021 environment package by running the installer:
python setup.py install
You will need Node.js version 12 or above: here
Python game interface
To directly use the ported game engine without the RL gym wrapper, here a couple example usages:
from luxai2021.game.game import Game
from luxai2021.game.actions import *
from luxai2021.game.constants import LuxMatchConfigs_Default
if __name__ == "__main__":
# Create a game
configs = LuxMatchConfigs_Default
game = Game(configs)
game_over = False
while not game_over:
print("Turn %i" % game.state["turn"])
# Array of actions for both teams. Eg: MoveAction(team, unit_id, direction)
actions = []
game_over = game.run_turn_with_actions(actions)
print("Game done, final map:")
print(game.map.get_map_string())
Python gym environment interface for RL
A gym interface and match controller was created that supports creating custom agents, and a framework to submit them in kaggle submissions. Keep in mind that this framework is built around one action per unit + city_tile that can act each turn. Creating a basic gym interface looks like the following, however you should look at the more complete example in the examples subfolder:
import random
from stable_baselines3 import PPO # pip install stable-baselines3
from luxai2021.env.lux_env import LuxEnvironment, SaveReplayAndModelCallback
from luxai2021.env.agent import Agent, AgentWithModel
from luxai2021.game.game import Game
from luxai2021.game.actions import *
from luxai2021.game.constants import LuxMatchConfigs_Default
from functools import partial # pip install functools
import numpy as np
from gym import spaces
import time
import sys
class MyCustomAgent(AgentWithModel):
def __init__(self, mode="train", model=None) -> None:
"""
Implements an agent opponent
"""
super().__init__(mode, model)
# Define action and observation space
# They must be gym.spaces objects
# Example when using discrete actions:
self.actions_units = [
partial(MoveAction, direction=Constants.DIRECTIONS.CENTER), # This is the do-nothing action
partial(MoveAction, direction=Constants.DIRECTIONS.NORTH),
partial(MoveAction, direction=Constants.DIRECTIONS.WEST),
partial(MoveAction, direction=Constants.DIRECTIONS.SOUTH),
partial(MoveAction, direction=Constants.DIRECTIONS.EAST),
SpawnCityAction,
]
self.actions_cities = [
SpawnWorkerAction,
SpawnCartAction,
ResearchAction,
]
self.action_space = spaces.Discrete(max(len(self.actions_units), len(self.actions_cities)))
self.observation_space = spaces.Box(low=0, high=1, shape=(10,1), dtype=np.float16)
def game_start(self, game):
"""
This function is called at the start of each game. Use this to
reset and initialize per game. Note that self.team may have
been changed since last game. The game map has been created
and starting units placed.
Args:
game ([type]): Game.
"""
pass
def turn_heurstics(self, game, is_first_turn):
"""
This is called pre-observation actions to allow for hardcoded heuristics
to control a subset of units. Any unit or city that gets an action from this
callback, will not create an observation+action.
Args:
game ([type]): Game in progress
is_first_turn (bool): True if it's the first turn of a game.
"""
return
def get_observation(self, game, unit, city_tile, team, is_new_turn):
"""
Implements getting a observation from the current game for this unit or city
"""
return np.zeros((10,1))
def action_code_to_action(self, action_code, game, unit=None, city_tile=None, team=None):
"""
Takes an action in the environment according to actionCode:
action_code: Index of action to take into the action array.
Returns: An action.
"""
# Map action_code index into to a constructed Action object
try:
x = None
y = None
if city_tile is not None:
x = city_tile.pos.x
y = city_tile.pos.y
elif unit is not None:
x = unit.pos.x
y = unit.pos.y
if city_tile != None:
action = self.actions_cities[action_code%len(self.actions_cities)](
game=game,
unit_id=unit.id if unit else None,
unit=unit,
city_id=city_tile.city_id if city_tile else None,
citytile=city_tile,
team=team,
x=x,
y=y
)
else:
action = self.actions_units[action_code%len(self.actions_units)](
game=game,
unit_id=unit.id if unit else None,
unit=unit,
city_id=city_tile.city_id if city_tile else None,
citytile=city_tile,
team=team,
x=x,
y=y
)
return action
except Exception as e:
# Not a valid action
print(e)
return None
def take_action(self, action_code, game, unit=None, city_tile=None, team=None):
"""
Takes an action in the environment according to actionCode:
actionCode: Index of action to take into the action array.
"""
action = self.action_code_to_action(action_code, game, unit, city_tile, team)
self.match_controller.take_action(action)
def game_start(self, game):
"""
This function is called at the start of each game. Use this to
reset and initialize per game. Note that self.team may have
been changed since last game. The game map has been created
and starting units placed.
Args:
game ([type]): Game.
"""
pass
def get_reward(self, game, is_game_finished, is_new_turn, is_game_error):
"""
Returns the reward function for this step of the game. Reward should be a
delta increment to the reward, not the total current reward.
"""
if is_game_finished:
if game.get_winning_team() == self.team:
return 1 # Win!
else:
return -1 # Loss
return 0
if __name__ == "__main__":
# Create the two agents that will play eachother
# Create a default opponent agent that does nothing
opponent = Agent()
# Create a RL agent in training mode
player = MyCustomAgent(mode="train")
# Create a game environment
configs = LuxMatchConfigs_Default
env = LuxEnvironment(configs=configs,
learning_agent=player,
opponent_agent=opponent)
# Play 5 games
env.reset()
obs = env.reset()
game_count = 0
while game_count < 5:
# Take a random action
action_code = random.sample(range(player.action_space.n), 1)[0]
(obs, reward, is_game_over, state) = env.step( action_code )
if is_game_over:
print(f"Game done turn {env.game.state['turn']}, final map:")
print(env.game.map.get_map_string())
obs = env.reset()
game_count += 1
# Attach a ML model from stable_baselines3 and train a RL model
model = PPO("MlpPolicy",
env,
verbose=1,
tensorboard_log="./lux_tensorboard/",
learning_rate=0.001,
gamma=0.998,
gae_lambda=0.95,
batch_size=2048,
n_steps=2048
)
print("Training model for 100K steps...")
model.learn(total_timesteps=10000000)
model.save(path='model.zip')
# Inference the agent for 5 games
game_count = 0
obs = env.reset()
while game_count < 5:
action_code, _states = model.predict(obs, deterministic=False)
(obs, reward, is_game_over, state) = env.step( action_code )
if is_game_over:
print(f"Game done turn {env.game.state['turn']}, final map:")
print(env.game.map.get_map_string())
obs = env.reset()
game_count += 1
Example python ML training
Create your own agent logic, observations, actions, and rewards by modifying this example:
https://github.com/glmcdona/LuxPythonEnvGym/blob/main/examples/agent_policy.py
Then train your model by:
python ./examples/train.py
You can then run tensorboard to monitor the training:
tensorboard --logdir lux_tensorboard
Example kaggle notebook
Here is a complete training, inference, and kaggle submission example in Notebook format:
https://www.kaggle.com/glmcdona/lux-ai-deep-reinforcement-learning-ppo-example
Preparing a kaggle submission
You have trained a model, and now you'd like to submit it as a kaggle submission. Here are the steps to prepare your submission.
Either view the above kaggle example or prepare a submission yourself:
- Place your trained model file as
model.zipand your agent fileagent_policy.pyin the./kaggle_submissions/folder. - Run
python download_dependencies.pyin./kaggle_submissions/to copy two required python package dependencies into this folder (luxai2021 and stable_baselines3). - Tarball the folder into a submission
tar -czf submission.tar.gz -C kaggle_submissions .
Important: The model.zip needs to have been trained on Python 3.7.* or you get a deserialization error, since this is the python version that Kaggle Environment uses to inference the model in submission.
Creating and viewing a replay
If you are using the example train.py to train your model, replays will be generated and saved along with a copy of the model every 100K steps. By default 5 replay matches will be saved with each model checkpoint into .\\models\\model(runid)_(step_count)_(rand).json to monitor your bot's behaviour. You can view the replay here: https://2021vis.lux-ai.org/
Alternatively to manually generate a replay from a model, you can place your trained model file as model.zip and your agent file agent_policy.py in the ./kaggle_submissions/ folder. Then run a command like the following from that directory:
lux-ai-2021 ./kaggle_submissions/main_lux-ai-2021.py ./kaggle_submissions/main_lux-ai-2021.py --maxtime 100000
This will battle your agent against itself and produce a replay match. This requires the official lux-ai-2021 to be installed, see instructions here: https://github.com/Lux-AI-Challenge/Lux-Design-2021
![[feature request] callback returning wins (or winrate) vs another agent](https://avatars.githubusercontent.com/u/43355664?v=4)
![[feature request] use legacy kind of agents to run on the new environment](https://avatars.githubusercontent.com/u/90915108?v=4)
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