Skip to content

Latest commit

 

History

History
59 lines (37 loc) · 3.72 KB

README.md

File metadata and controls

59 lines (37 loc) · 3.72 KB
Raw

Behavioural cloning experiments with video games

Code used for the paper "Benchmarking End-to-End Behavioural Cloning on Video Games". See examples of the agents playing here.

Behavioural cloning experiments with Atari 2600 games, ViZDoom and modern video games with ViControl.

The experiments can be run with the run_*.sh scripts.

Atari experiments

The Atari 2600 experiments can be run with the run_atari_head.sh, run_atarigc.sh and run_atari_delayed.sh scripts. These scripts require the Atari Grand Challenge and Atari-HEAD datasets to be available in the paths pointed to by the ATARI_GC_DIR and ATARI_HEAD_DIR environment variables. The Atari Grand Challenge dataset can be downloaded at http://atarigrandchallenge.com/data and the Atari-HEAD dataset at https://zenodo.org/record/3451402.

The ATARI_GC_DIR environment variable should point at the root directory of the extracted dataset, which contains the screens and trajectories subdirectories.

The ATARI_HEAD_DIR environment variable should point at a directory that has subdirectories for each game (i.e. montezuma_revenge, ms_pacman and space_invaders in our experiments). The .tar.bz2 archives inside each game's directory should also be extracted.

ViZDoom experiments

To gather data from a human player:

mkdir -p doom_recordings
python3 record_vizdoom.py --config doom_scenarios/health_gathering_supreme.cfg --num-games 20 --output doom_recordings
python3 record_vizdoom.py --config doom_scenarios/deathmatch.cfg --num-games 10 --output doom_recordings

After this, run_vizdoom.sh will train models with behavioural cloning and evaluate their performance. Models and evaluation logs will appear under experiments directory.

ViControl experiments

Before the ViControl scripts can be used, the ViControl binaries and messages_pb2.py must be copied to the video_game_env directory. The Windows binaries and messages_pb2.py can be downloaded from the ViControl releases page. Binaries for other platforms can be built by following the instructions in the project README file.

To record data from game, you need the name of the process. E.g. to capture data from Super Hexagon (make sure vicontrol_recordings directory exists):

python3 .\record_vizdoom.py superhexagon.exe SuperHexagon ./vicontrol_recordings

After gathering data, recorded buttons have to be converted to with utils/convert_recordings_to_multidiscrete.sh script:

mkdir -p ./vicontrol_recordings/SuperHexagon/trajectories
./utils/convert_recordings_to_multidiscrete.sh SuperHexagon ./vicontrol_recordings/SuperHexagon/trajectories_pressed_buttons ./vicontrol_recordings/SuperHexagon/trajectories

Now you can train the behavioural cloning model. Note that you have to tweak resolution to be appropiate for the game (e.g. does not blur too much). This example uses the parameters used in the paper:

mkdir -p ./experiments/vicontrol_models
python3 train.py ./vicontrol_recordings SuperHexagon ./experiments/vicontrol_models/superhexagon --epochs 30 --workers 4 --framestack 1 --l2 0.00001 --save-freq 30 --json --width 95 --height 60 --actions 2 --fileformat jpg

Finally, you can use the model to play Super Hexagon. Note that you have to set the correct resolution again.

python3 .\play_vicontrol.py --process superhexagon.exe --game SuperHexagon ./experiments/vicontrol_models/superhexagon --width 95 --height 60

To add support for more games, modify utils/game_keyboard_mapping.py to list what buttons should be tracked.