Ll7/issue48 retrain the robot with a reward that penalizes quick action changes #54
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- punish changes in the action to get a smoother policy
…taken by the robot
This commit adds a new script, `user_queue.sh`, to the SLURM directory. The script allows users to view their own job queue by running the `squeue -u $USER` command.
This commit adds a new SLURM training script, `slurm_train.sl`, which includes the necessary SLURM directives and commands to run a Python training script on a GPU node. The script sets the job name, partition, time limit, memory per CPU, number of CPUs, and GPU resources. It also loads the required modules and activates the specified conda environment. Finally, it runs the `slurm_PPO_robot_sf.py` Python script using `srun`.
The SLURM training script `slurm_PPO_robot_sf.py` has been added to the repository. This script is used to train a PPO model on the robot_sf environment. It includes the necessary SLURM directives and commands to run the training script on a GPU node. The script sets the job name, partition, time limit, memory per CPU, number of CPUs, and GPU resources. It also loads the required modules and activates the specified conda environment. Finally, it runs the PPO training with the specified parameters.
The `.cursorrules` file has been added to the repository. This file contains a description of the AI assistant's approach to Python development, including project structure, modular design, configuration management, error handling, testing, documentation, and more.
… for robot_sf training
WalkthroughThe changes introduce a comprehensive set of guidelines for Python development, emphasizing best practices in project structure, error handling, testing, and CI/CD implementation. New scripts facilitate environment setup, job submission, and training of reinforcement learning models, while modifications enhance existing functionalities in environment management and reward calculations. Additionally, updates to Changes
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Actionable comments posted: 1
Outside diff range and nitpick comments (2)
.gitignore (1)
14-14: Consider removing the comment.The comment
# modelis not necessary as the next line clearly specifies themodeldirectory to be ignored. Removing the comment would not affect the functionality of the.gitignorefile.Apply this diff to remove the comment:
-# modelSLURM/slurm_PPO_robot_sf.py (1)
23-61: Consider making the training parameters configurable and breaking down the function into smaller, more focused functions.The
trainingfunction is well-structured and follows a logical flow for setting up and training the PPO model. The use of parallel environments and custom feature extractor can enhance the learning process, and the checkpoint and metrics callbacks provide useful functionality for saving the model and tracking performance.However, consider the following improvements:
- Make the training parameters, such as the number of timesteps and the save directory, configurable. This will allow for easier experimentation and tuning of the training process.
- Break down the function into smaller, more focused functions for better readability and maintainability. For example, you could extract the environment setup logic into a separate function.
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Files ignored due to path filters (1)
model/ppo_model_retrained_10m_2024-09-17.zipis excluded by!**/*.zip
Files selected for processing (12)
- .cursorrules (1 hunks)
- .gitignore (1 hunks)
- SLURM/.gitignore (1 hunks)
- SLURM/load_module.sh (1 hunks)
- SLURM/readme.md (1 hunks)
- SLURM/slurm_PPO_robot_sf.py (1 hunks)
- SLURM/slurm_train.sl (1 hunks)
- SLURM/user_queue.sh (1 hunks)
- git_submodule.sh (1 hunks)
- robot_sf/gym_env/reward.py (2 hunks)
- robot_sf/gym_env/robot_env.py (1 hunks)
- scripts/PPO_training/train_ppo_punish_action.py (1 hunks)
Files skipped from review due to trivial changes (3)
- SLURM/.gitignore
- SLURM/readme.md
- SLURM/user_queue.sh
Additional context used
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SLURM/slurm_PPO_robot_sf.py
7-7:
datetime.datetimeimported but unusedRemove unused import:
datetime.datetime(F401)
9-9:
gymnasiumimported but unusedRemove unused import:
gymnasium(F401)
Additional comments not posted (17)
SLURM/load_module.sh (4)
1-1: LGTM!The shebang line is correct and specifies the bash interpreter.
3-3: LGTM!Purging all currently loaded modules is a good practice to ensure a clean environment before loading new modules.
5-5: LGTM!Loading the
anacondaandcudamodules is a common practice to set up a Python environment and use NVIDIA GPUs.
7-7: Verify the existence of theconda_envenvironment.Activating the
conda_envconda environment is necessary to use the packages installed in that environment.Run the following script to verify the existence of the
conda_envenvironment:git_submodule.sh (1)
1-4: LGTM!The shell script is well-structured and serves a clear purpose. Here's a breakdown:
- The shebang line
#!/bin/bashcorrectly specifies the interpreter for the script.- The comment provides a concise and helpful explanation of when to use the script.
- The
git submodule update --init --recursivecommand is the appropriate way to initialize and update all submodules in a Git repository.Overall, this script provides a convenient solution for users who may have forgotten to clone a repository recursively when it contains submodules. It ensures that all nested repositories are properly initialized and synchronized with the main repository.
.gitignore (1)
15-15: LGTM!The line correctly specifies the
SLURM/modeldirectory to be ignored by Git. This is likely done to prevent tracking of files generated or used by the SLURM workload manager.SLURM/slurm_train.sl (1)
1-30: Excellent job setting up the SLURM script for the robot simulation training!The script is well-structured and follows best practices for configuring a SLURM job. Here are some key strengths:
- The job parameters are appropriately set for the specific training task, ensuring efficient utilization of computational resources.
- The script takes care of environment setup, including module management and conda environment activation, which is crucial for reproducibility and avoiding conflicts.
- The commented-out guidance on OpenMP threads is helpful for users who may need to optimize performance based on their parallelization strategy.
- The training script is executed using the
sruncommand, which is suitable for running parallel jobs in a SLURM environment.Overall, the script is well-written and should effectively facilitate the execution of the robot simulation training job using the PPO algorithm.
.cursorrules (1)
1-1: Excellent set of guidelines for Python development!The outlined guidelines provide a solid foundation for maintaining a high-quality, maintainable, and AI-friendly Python codebase. The emphasis on clear project structure, modular design, and separation of concerns will greatly improve code organization and readability.
The inclusion of typing annotations and adherence to PEP 257 convention for docstrings will enhance code documentation and make it easier for developers, including AI assistants, to understand and work with the codebase. The focus on comprehensive testing using pytest and the requirement for fully annotated and documented tests will ensure code quality and reliability.
The use of environment variables for configuration management, robust error handling and logging, dependency management using requirements.txt and virtual environments, and the implementation of CI/CD practices using GitHub Actions are all excellent practices that will contribute to a stable, secure, and efficient development workflow.
Overall, adhering to these guidelines will result in a high-quality Python codebase that is well-structured, maintainable, and optimized for AI-assisted development.
scripts/PPO_training/train_ppo_punish_action.py (2)
13-52: LGTM! The training routine is well-implemented and aligns with the PR objective.The
trainingfunction sets up a comprehensive training routine for the PPO algorithm, utilizing best practices for parallel training and custom feature extraction. Key aspects of the implementation include:
- Efficient parallel training using
make_vec_envandSubprocVecEnv.- Custom feature extractor (
DynamicsExtractor) specified in the policy arguments to enhance the agent's learning capabilities.- Custom reward function (
punish_action_reward) that aligns with the PR objective of penalizing rapid changes in actions taken by the robot.- Callbacks for saving model checkpoints and collecting driving metrics.
- Reasonable training hyperparameters, such as the total number of timesteps.
- Model saved to the correct directory upon completion of the training process.
Overall, the training routine is well-structured and incorporates the necessary components to effectively train the robot using the PPO algorithm while addressing the specific requirements outlined in the PR objective.
19-26: LGTM! The environment creation logic is well-encapsulated and maintainable.The
make_envfunction properly encapsulates the environment creation logic, making it reusable and maintainable. The benefits of this approach include:
- Centralized environment configuration using the
EnvSettingsclass, allowing for easy modification if needed.- Flexibility in defining the reward structure by passing the custom reward function as an argument to the
RobotEnvconstructor.- Clear separation of concerns between the environment creation and the training routine.
The function correctly sets up the environment configuration based on the specified pedestrian densities and difficulty level, and returns an instance of the
RobotEnvwith the custom reward function.SLURM/slurm_PPO_robot_sf.py (1)
29-33: LGTM!The
make_envfunction is a simple and effective factory function for creating instances of theRobotEnvenvironment with specific configuration settings. The use of theEnvSettingsclass to configure the environment is a good practice.robot_sf/gym_env/reward.py (1)
44-84: LGTM! The new reward function aligns with the PR objective.The
punish_action_rewardfunction correctly incorporates the action punishment logic while reusing the existingsimple_rewardfunction. Here are the key aspects of the implementation:
- The action punishment is only applied when
punish_actionisTrueandlast_actionis available.- The Euclidean distance between the current and last action is used to measure the action difference, which captures the magnitude of the change.
- The penalty is proportional to the action difference, scaled by
punish_action_penalty, allowing for tuning the strength of the punishment.Overall, the changes align with the PR objective of penalizing quick action changes to encourage more stable and deliberate actions from the robot.
robot_sf/gym_env/robot_env.py (5)
149-155: LGTM! Thereward_dictis a great improvement over themetadictionary.The
reward_dictprovides more context to the reward function, enabling the implementation of more sophisticated reward functions that can take into account the action history. This is particularly important for penalizing quick action changes, as mentioned in the PR summary.The changes also improve the readability and maintainability of the code by using a more descriptive variable name.
159-161: LGTM! Updating thelast_actionfor the next step is crucial.Updating the
last_actionfor the next step ensures that the reward function always has access to the previous action, which is necessary for penalizing quick action changes, as mentioned in the PR summary.The changes enable the implementation of reward functions that can take into account the action history and encourage more stable and deliberate actions from the robot.
167-168: LGTM! Aligning with the gym interface and returning theinfodictionary is a good practice.Including the
Falsevalue for the truncated flag aligns the return statement with the expected output format for gym environments, improving compatibility and consistency. This makes it easier to use the environment with existing tools and libraries.Returning the
infodictionary provides flexibility for including additional information about the environment state, which can be useful for debugging, logging, or other purposes. The dictionary currently contains thestepandmetainformation from thereward_dict, but it can be extended to include other relevant information in the future.
170-179: LGTM! Aligning with the gym interface and resetting thelast_actionis a good practice.Accepting optional
seedandoptionsparameters in theresetmethod aligns with the gym interface and provides flexibility for controlling the environment reset behavior. This allows users to specify a seed for reproducibility or provide additional options to customize the reset process.Calling the
super().resetmethod with theseedandoptionsparameters ensures that the base class reset functionality is properly invoked.Resetting the
last_actiontoNoneensures a clean slate for each new episode, preventing any unintended influence from previous actions. This is important for ensuring that each episode is independent and not affected by the action history of previous episodes.
187-190: LGTM! Returning theinfodictionary from theresetmethod is a good practice.Returning the
infodictionary from theresetmethod aligns with the gym interface and provides a consistent structure for returning additional information. This improves the compatibility and consistency of the environment with the gym interface.While the current implementation uses a placeholder string for the
"info"key, the presence of theinfodictionary provides a foundation for future enhancements. In the future, more meaningful information can be included in theinfodictionary to provide additional insights into the environment state upon reset.Consider adding a TODO comment to indicate the potential for future enhancements using the
infodictionary.
SLURM/slurm_PPO_robot_sf.py
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| import gymnasium as gym |
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Address the static analysis hints.
The static analysis tool suggests removing unused imports:
- The
datetimeimport is indeed unused in the code and can be safely removed. - The
gymnasiumimport is used indirectly through themake_vec_envfunction fromstable_baselines3.common.env_util. Verify if this import is necessary for themake_vec_envfunction to work correctly before removing it.
Apply this diff to remove the unused datetime import:
-from datetime import datetimeCommittable suggestion was skipped due to low confidence.
Tools
Ruff
7-7:
datetime.datetimeimported but unusedRemove unused import:
datetime.datetime(F401)
9-9:
gymnasiumimported but unusedRemove unused import:
gymnasium(F401)
The SLURM training script `slurm_PPO_robot_sf.py` has been refactored to improve code readability and maintainability. The unnecessary import statements have been removed, and the script now follows PEP 8 style guidelines. This refactor does not change the functionality of the script, but it enhances the overall quality of the codebase.
…t_can_simulate_with_pedestrians()
…on variable during reset
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Actionable comments posted: 0
Outside diff range and nitpick comments (3)
tests/env_test.py (2)
16-16: LGTM! Consider utilizing the 'info' variable.The change correctly handles the new return value of
env.reset(), which now includes aninfodictionary. This aligns with common practices in reinforcement learning environments.Consider adding an assertion to verify the structure or content of the
infodictionary. This would make the test more robust and document the expected content of theinforeturn value. For example:assert isinstance(info, dict), "info should be a dictionary" assert "some_expected_key" in info, "info should contain 'some_expected_key'"
30-31: LGTM! Consider adding assertions for terminated and truncated.The change correctly handles the new return values of
env.step(), separating 'terminated' and 'truncated' states. This aligns with the Gymnasium API and provides more detailed information about episode endings.To make the test more robust, consider adding assertions to verify that 'terminated' and 'truncated' are boolean values. This would help catch any unexpected changes in the step() method's return types. For example:
assert isinstance(terminated, bool), "terminated should be a boolean" assert isinstance(truncated, bool), "truncated should be a boolean"tests/sb3_test.py (1)
26-26: LGTM! Consider utilizing theinfovariable in future tests.The change correctly adapts to the new signature of the
resetmethod, capturing bothobsandinfo. This is a good practice to handle all returned values.While the current implementation is correct, consider the following suggestions for future improvements:
- You might want to add assertions or checks using the
infovariable in this or future tests to ensure it contains expected data.- If
infois not needed in this particular test, you could use_as a placeholder to explicitly indicate that it's intentionally unused:obs, _ = inf_env.reset()
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Files selected for processing (3)
- robot_sf/gym_env/robot_env.py (1 hunks)
- tests/env_test.py (2 hunks)
- tests/sb3_test.py (1 hunks)
Files skipped from review as they are similar to previous changes (1)
- robot_sf/gym_env/robot_env.py
Additional comments not posted (1)
tests/env_test.py (1)
Line range hint
1-34: Verify method signature changes in RobotEnv class.The changes in this test file correctly handle the updated return values of
env.reset()andenv.step(). However, the AI summary mentions changes to method signatures that are not visible in this file.To ensure consistency across the codebase, please run the following script to verify the method signature changes in the RobotEnv class:
This script will help confirm that the method signatures have been updated as mentioned in the AI summary.
ll7
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ll7/issue48-Retrain-the-robot-with-a-reward-that-penalizes-quick-action-changes
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