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rlgameauthors

rlgameauthors is the replication package of the work “Using Reinforcement Learning for Load Testing of Video Games”.

The purpose of this repository is to provide code and data to replicate our results.

Requirements

  • requirements_study1.txt provides the list of libraries needed for study1.
  • requirements_study2.txt provides the list of libraries needed for study2.

In order to install the requirements run the following command in your virtual environment:

pip install -r requirements_study<x>.txt 

Content

CartPole

The CartPole folder contains everything needed to replicate study 1 on CartPole game.

  • CartPole_RELINE_train.py is the script to train the agent using the RELINE approach for 200 steps. It provides as output model_RELINE (i.e., the model trained)
  • CartPole_RL-baseline_train.py is the script to train the agent using the RL-baseline for 200 steps. It provides as output model_RL-baseline.
  • CartPole_RELINE_1k_episodes.py is the script to play additional 1000 episodes (still training) with model_RELINE. It provide as output information about the injected bugs spotted.
  • CartPole_RL-baseline_1k_episodes.py is the script to play additional 1000 episodes (still training) with model_RL-baseline. It provide as output information about the injected bugs spotted.
  • CartPole_Random_1k_episodes.py is the script to play 1000 episodes with a random agent. It provides as output information about the injected bugs spotted.
  • results.xlsx contains our results obtained with the 3 different approaches.
  • RL-baseline_agent_playing.mp4 is a video of the RL-baseline agent playing CartPole.

MsPacman

The MsPacman folder contains everything needed to replicate study 1 on MsPacman game.

  • ‘msPacman_RELINE.py’ is the script to train the agent using the RELINE approach for 1000 steps and to play additional 1000 episodes (still training), used to create a report of the injected bugs spotted. It provides as output the best (MsPacmanNoFrameskip-v4-best_RELINE.dat) and the last (MsPacmanNoFrameskip-v4-last_RELINE.dat) model trained and information about the injected bugs spotted.
  • msPacman_RL-baseline.py is the script to train the agent using the RL-baseline for 1000 steps and to play additional 1000 episodes (still training), used to create a report of the injected bugs spotted. It provides as output the best (MsPacmanNoFrameskip-v4-best_RL-baseline.dat) and the last (MsPacmanNoFrameskip-v4-last_RL-baseline.dat) model trained and information about the injected bugs spotted.
  • msPacman_Random.py is the script to play 1000 episodes with a random agent. It provides as output information about the injected bugs spotted.
  • bug_left and bug_right folders contain images used to check if the agent is in one of the injected bug locations.
  • lib folder contains files used by python scripts to define the DQN model used by the RELINE approach and the RL-baseline and the game environment.
  • results.xlsx contains our results obtained with the 3 different approaches.
  • RL-baseline_agent_playing.mp4 is a video of the RL-baseline agent playing MsPacman.

SuperTuxKart

The SuperTuxKart folder contains everything needed to replicate study 2 on SuperTuxKart game.

  • 300episodes_RL-baseline_agent.xlsx, 300episodes_same_actions.csv files containing information about the rendering time of frames collected during 300 episodes played by the RL-baseline best agent and 300 episodes played performing always the same actions respectively. We used this information to verify the stability of the metric used and to evaluate the threshold needed for the RELINE approach’s reward function.

  • 300episodes_RL-baseline_agent.png, 300episodes_same_actions.png images graphically showing the information about the rendering time of frames contained in the 300episodes_RL-baseline_agent.csvand300episodes_same_actions.csv` files.

  • FPS_info_RELINE.xlsx, FPS_info_RL-baseline.xlsx, FPS_info_Random.xlsx files containing information about the rendering time of frames collected during episodes played by the different agents trained with the 3 different approaches (RELINE, RL-baseline, Random).

  • RELINE_training.png, RL-baseline_training.png images showing the mean reward (of last 100 games) during the training of the agents using the RELINE approach and the RL-baseline.

  • RL-baseline_agent_playing.avi is a video of the RL-baseline agent playing SuperTuxKart.

  • code:

    • agents:
      • SuperTuxKart_RELINE.py is the script to train the agent using the RELINE approach for 2300 steps and to play additional 1000 episodes (still training), used to create the report of the low-FPS positions spotted. It provides as output the best (SuperTuxKart-best_RELINE.dat) and the last (SuperTuxKart-last_RELINE.dat) model trained and information about the rendering time of frames collected during the episodes.
      • SuperTuxKart_RL-baseline.py is the script to train the agent using the RL-baseline for 2300 steps and to play additional 1000 episodes (still training), used to create the report of the low-FPS positions spotted. It provides as output the best (SuperTuxKart-best_RL-baseline.dat) and the last (SuperTuxKart-last_RL-baseline.dat) model trained and information about the rendering time of frames collected during the episodes.
      • SuperTuxKart_Random.py is the script to play 1000 episodes with a random agent. It provides as output information about the rendering time of frames collected during the episodes.
      • dqn_model.py is the python file defining the DQN model used by the RELINE approach and the RL-baseline.
    • gym-master is the repository containing the wrapper of SuperTuxKart game. Follow the instructions contained in the README file to install it.

    Once the wrapper has been installed, move the agent folder in gym-mastar/gym/.

    To obtain reliable data (rendering time of frames) run the scripts using the chrt --rr 1 option, that in Linux maximizes the priority of the process. Also, make sure that no other processes (besides the ones run by the OS) are running on the machine.