ai-doom

The training agent wrapper for Vizdoom environment.

Table of Contents

1. Installation
2. Project Motivation
3. Running
4. Licensing, Authors and Acknowledgements

Installation

For the beginning install the Vizdoom - follow the instructions for your platform. I'm bound to Windows for some time - I was using the built binary prepared by the author (available on that project repo).

Before installing the requirement install the pytorch on your own (you can get there the best configuration on your platform/requirements) - installing directly using the command below will install the default versions for your pip (which you might not want to have).

Then just run:

pip install -r requirements.txt

Project Motivation

During work on the AI course I found it's content outdated when it comes to the used libraries (the core code is in 40% from there, but it's wrapped in custom implementations).

Running

To run the most basic example just call:

python trainer.py --agent_name basic --scenario basic

This will call agent training using the basic agent along with the most basic scenario from Vizdoom.

For script trainer.py the available options are:

• --agent_name - (mandatory) the name of the agent, by default the available are: 'basic' and 'linear'.
• --agent_model_path - the path to the serialized agent and optimizer.
• --scenario - (mandatory) the scenario to train - available are (.cfg files are Vizdoom scenarios names): 'basic' (basic.cfg), 'rocket' (rocket_basic.cfg) and 'corridor' (deadly_corridor.cfg).
• --starting_epoch - number of the epoch that we are going to start.
• --nb_epochs - for how many epochs we are going to train the agent.
• --n_step - how many steps the agent will perform in the environment before saving them as a memory record.
• --n_steps - how many records will be saved to the memory before training will happen.
• --memory_capacity - how many entries to memory we will have.
• --memory_path - path to the memory file, is set to None the learning will start with empty memory.
• --image_dim - the dimension in pixels of read image (we are reading them as square)

To play trained agent we can use script player.py for which the options are:

• --scenario - (mandatory) same as for 'trainer.py'
• --agent_name - (mandatory) same as for 'trainer.py'
• --agent_model_path - (mandatory) same as for 'trainer.py'
• --image_dim - same as for 'trainer.py'
• --nb_episodes - how many episodes (full gane ended with reaching goal/losing) we play

Licensing, Authors and Acknowledgements

Project is released under MIT License I would like to get information whether the project was useful for you. In case of issues/bug don't hestitate to use the issue tracker for this repo on github.