tnt-battlesnake

This repository contains a Battlesnake OpenAI Gym environment and Reinforcement Learning Agents, that operate in this environment.

Quick start

# Create conda environment
conda create -n bs python=3.6 pip

# Activate environment
conda activate bs

# Install dependencies
pip install sacred gym black pylint tensorflow keras tensorboardX opencv-python gym[atari] pygame

# Train a DQN agent
python run.py train_dqn

# Or run example random agent
python run.py example

# In another terminal window
tensorboard --logdir tmp

Repository structure

• battlesnake (WIP)
  • Adapter to use the trained agent in the Battlesnake game.
• common
  • Common models and functions that can be shared between multiple RL algorithms.
• dqn
  • Contains a DQN agent with several improvements.
• example
  • An example of a randomly acting agent that can be used with the gym environment.
• experiments
  • Experiments should have an unique name and a main.py file which defines a sacred Experiment called ex for reproducibility.
• gym_battlesnake
  • An OpenAI gym environment for Battlesnake.

Distributed DQN

# Start Learner
python run.py train_dqn_distributed

# In other terminals
python run.py train_dqn_distributed --actor [number] --skip_observe --learner_address [IP address]

# Or automated via ssh
python start_training.py -l [gpu server] -a [worker server 1] [worker server 2] ... -p [number of actors on worker 1] [number of actors on worker 2]... --gpus [ID of the GPUs]