Code for "Implementation Matters in Deep RL: A Case Study on PPO and TRPO"

This repository contains our implementation of PPO and TRPO, with manual toggles for the code-level optimizations described in our paper. We assume that the user has a machine with MuJoCo and mujoco_py properly set up and installed, i.e. you should be able to run the following command on your system without errors:

import gym
gym.make_env("Humanoid-v2")

The code itself is quite simple to use. To run the ablation case study discussed in our paper, you can run the following list of commands:

1. cd src/case_study_experiment/
2. python setup_agents.py
3. cd ../
4. Edit the NUM_THREADS variables in the run_agents.py file according to your local machine.
5. Train the agents: python run_agents.py case_study_experiment/agent_configs
6. Open the case_study_experiment/Plot_Results.ipynb notebook to browse the results of the experiment as a pandas DataFrame.

You can easily make a new X_experiment folder to train new agents. For example, to run a grid search for PPO-M hyperparameters, one could create a new X_experiment folder, and can edit the PARAMS variable in setup_agents.py as follows:

PARAMS = {
    "game": ["Humanoid-v2"], # Can be any MuJoCo environment
    "mode": ["ppo"],
    "out_dir": ["X_experiment/agents"],
    "norm_rewards": ["none"],
    "initialization": ["xavier"],
    "anneal_lr": [False],
    "value_clipping": [False],
    "ppo_lr_adam": iwt(1e-5, 2.9e-4, 7e-5, 5),
    "val_lr": [1e-4, 2e-4, 3e-4],
    "cpu": [True], # If false, try to use GPU 
    "advanced_logging": [False] # Whether to log trust region stats (KL, ratio),
}

See the MuJoCo.json file for a full list of adjustable parameters.

We have provided the following folders which contain setup and analysis files for all the experiments in our paper:

• Ablation study: case_study_experiment
• Grid searches for PPO/PPO-M/PPO-NoClip/TRPO/TRPO+: get_rewards_experiment
• KL and Ratio plots: trust_region_experiment

Any of these folders can be run using steps (1-6) above to recreate the results of our work (though the get_rewards_experiment does one algorithm at a time, so one would have to change the PARAMS attribute a few times to capture all the algorithms)