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Status: Archive (code is provided as-is, no updates expected)

Phasic Policy Gradient

[Paper]

This is code for training agents using Phasic Policy Gradient (citation).

Supported platforms:

• macOS 10.14 (Mojave)
• Ubuntu 16.04

Supported Pythons:

• 3.7 64-bit

Install

You can get miniconda from https://docs.conda.io/en/latest/miniconda.html if you don't have it, or install the dependencies from environment.yml manually.

git clone https://github.com/openai/phasic-policy-gradient.git
conda env update --name phasic-policy-gradient --file phasic-policy-gradient/environment.yml
conda activate phasic-policy-gradient
pip install -e phasic-policy-gradient

Reproduce and Visualize Results

PPG with default hyperparameters (results/ppg-runN):

mpiexec -np 4 python -m phasic_policy_gradient.train
python -m phasic_policy_gradient.graph --experiment_name ppg

PPO baseline (results/ppo-runN):

mpiexec -np 4 python -m phasic_policy_gradient.train --n_epoch_pi 3 --n_epoch_vf 3 --n_aux_epochs 0 --arch shared
python -m phasic_policy_gradient.graph --experiment_name ppo

PPG, varying E_pi (results/e-pi-N):

mpiexec -np 4 python -m phasic_policy_gradient.train --n_epoch_pi N
python -m phasic_policy_gradient.graph --experiment_name e_pi

PPG, varying E_aux (results/e-aux-N):

mpiexec -np 4 python -m phasic_policy_gradient.train --n_aux_epochs N
python -m phasic_policy_gradient.graph --experiment_name e_aux

PPG, varying N_pi (results/n-pi-N):

mpiexec -np 4 python -m phasic_policy_gradient.train --n_pi N
python -m phasic_policy_gradient.graph --experiment_name n_pi

PPG, using L_KL instead of L_clip (results/ppgkl-runN):

mpiexec -np 4 python -m phasic_policy_gradient.train --clip_param 0 --kl_penalty 1
python -m phasic_policy_gradient.graph --experiment_name ppgkl

PPG, single network variant (results/ppgsingle-runN):

mpiexec -np 4 python -m phasic_policy_gradient.train --arch detach
python -m phasic_policy_gradient.graph --experiment_name ppg_single_network

Pass --normalize_and_reduce to compute and visualize the mean normalized return with phasic_policy_gradient.graph.

Citation

Please cite using the following bibtex entry:

@article{cobbe2020ppg,
  title={Phasic Policy Gradient},
  author={Cobbe, Karl and Hilton, Jacob and Klimov, Oleg and Schulman, John},
  journal={arXiv preprint arXiv:2009.04416},
  year={2020}
}