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CODING STYLE

If you are contributing to RL-Studio tool development, please follow the below coding style and structure of directories, files recommendations:

SOME RULES

  • PEP 8 style guide for Python Code

  • Black format

  • Pylint as static code analyser

  • Constants variable must be upper-case (e.g TIME_THRESHOLD, NUMBER_OF_RUNS)

  • Comment all non trivial functions to ease readability

  • All the internal imported packages must be imported from the root of the project (e.g import rl_studio.agents instead import agents)

  • Organize imports before pushing your code to the repo

  • When creating a project, please keep in mind:

    • in /agents directory, files names should be mode_task_algorithm_simulator_framework.py, i.e. trainer_followline_ddpg_F1_gazebo_tf.py or inferencer_mountaincar_qlearn_openai_pytorch.py. In case of not using framework leave it blank.
    • in /envs/gazebo/f1/models directory, files names should be task_algorithm_framework.py, i.e. followline_ddpg_gazebo_tf.py or followlane_qlearn_pytorch.py. In case of not using framework leave it blank.
    • As a general rule, classes names have to follow convention ModeTaskAlgorithmAgentSimuladorFramework, i.e. TrainerFollowLaneDDPGF1GazeboPytorch or InferencerFollowLaneDQNF1GazeboTF
    • in /envs/gazebo directory, classes names follow rule TaskAlgorithmAgentSimulatorFramework, i.e. FollowlineDDPGF1GazeboTF.

Directory architecture

Config files

  • in /config directory add a configuration file with the following format config_mode_task_algorithm_agent_simulator.yaml, i.e. config_training_followlane_qlearn_F1_carla.yaml

Models

  • Add a trained brain in /checkpoints folder. You can configure it in the config.yaml file. Automatically the app will add a directory with the format task_algorithm_agent_simulator_framework where to save models.
  • The file model should have the format timestamp_maxreward_epoch_ADITIONALTEXT.h5 in format h5 i.e. 09122002_max45678_epoch435_actor_conv2d32x64_critic_conv2d32x64_actionsCont_stateImg_rewardDiscrete.h5 to indicate the main features of the model saved in order to easily find the exact model.

Metrics

  • In /metrics folder should be saved statistics and metrics of models. You can configure it in the config.yaml file. Automatically the app will add a directory with the format mode/task_algorithm_agent_simulator_framework/data where to save data.

Graphics

  • In /metrics folder should be saved graphics of models. You can configure it in the config.yaml file. Automatically the app will add a directory with the format mode/task_algorithm_agent_simulator_framework/graphics where to save graphics.

Logs and TensorBoard files

  • In /logs folder should be saved TensorBoard and logs files. You can configure it in the config.yaml file. For TensorBoard, automatically the app will add a directory with the format mode/task_algorithm_agent_simulator_framework/TensorBoard.

    For logs, the app automatically will add a directory with the format mode/task_algorithm_agent_simulator_framework/logs.

TIPS

  • You can refer to "mountain_car qlearning" project as an example of how to implement a real time monitoring
  • You can refer to "cartpole dqn" project as an example of how to implement logging