Gym-Battlesnake is a multi-agent reinforcement learning environment inspired by the annual Battlesnake event held in Victoria, BC each year, and conforming to the OpenAI Gym interface.
- Multi-threaded game implementation written in fast C++
- Single agent training with multiple other agents as opponents
- Render mode available to see your agents play
Gym-Battlesnake has only been tested on Ubuntu 18.04. Install the dependencies using the command:
sudo apt-get update && sudo apt-get install cmake libopenmpi-dev python3-dev zlib1g-dev libsfml-dev
You will also need to install tensorflow or tensorflow-gpu (2.0 not tested), see https://www.tensorflow.org/install/pip.
Clone this repository using the following command:
git clone https://github.com/ArthurFirmino/gym-battlesnakeChange into the directory and install using pip (consider setting up a Python virtual environment first):
cd gym-battlesnake
pip install -e .Single agent training:
from gym_battlesnake.gymbattlesnake import BattlesnakeEnv
from gym_battlesnake.custompolicy import CustomPolicy
from stable_baselines import PPO2
env = BattlesnakeEnv(n_threads=4, n_envs=16)
model = PPO2(CustomPolicy, env, verbose=1, learning_rate=1e-3)
model.learn(total_timesteps=100000)
model.save('ppo2_trainedmodel')
del model
model = PPO2.load('ppo2_trainedmodel')
obs = env.reset()
for _ in range(10000):
action,_ = model.predict(obs)
obs,_,_,_ = env.step(action)
env.render()Multi agent training:
from gym_battlesnake.gymbattlesnake import BattlesnakeEnv
from gym_battlesnake.custompolicy import CustomPolicy
from stable_baselines import PPO2
num_agents = 4
placeholder_env = BattlesnakeEnv(n_threads=4, n_envs=16)
models = [PPO2(CustomPolicy, placeholder_env, verbose=1, learning_rate=1e-3) for _ in range(num_agents)]
placeholder_env.close()
for _ in range(10):
for model in models:
env = BattlesnakeEnv(n_threads=4, n_envs=16, opponents=[ m for m in models if m is not model])
model.set_env(env)
model.learn(total_timesteps=100000)
env.close()
model = models[0]
env = BattlesnakeEnv(n_threads=1, n_envs=1, opponents=[ m for m in models if m is not model])
obs = env.reset()
for _ in range(10000):
action,_ = model.predict(obs)
obs,_,_,_ = env.step(action)
env.render()- See OpenAI documentation on
gymfor more details about its interface - See
stable-baselinesdocumentation for more details on their PPO2 implementation and other suitable algorithms - For multi-agent training
tensorflow-gpuis recommended, as well as a large number of environments (~100) to maximize data transfer to the GPU.
- Fork
- Clone and Setup
- Develop
- Pull Request
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