4SkyNet / POP3D

Policy Optimization with Penalized Point Probability Distance: an Alternative to Proximal Policy Optimization

POP3D

Source code(Tensorflow)for Policy Optimization With Penalized Point Probability Distance: An Alternative To Proximal Policy Optimization (https://arxiv.org/abs/1807.00442v2)

Prerequisite

• gym[mujoco,atari]
• scipy
• tqdm
• joblib
• zmq
• dill
• mpi4py
• cloudpickle
• tensorflow>=1.4.0
• opencv-python

Training

If you desire to run all games(49 Atari or 7 Mujoco) using 2 GPUs( You can change the task distribution work based on GPU resource), you can do as follows.

• Atari

python -m baselines.ppo2.run_all_atari

• Mujoco

python -m baselines.ppo2.run_all_mujoco

If you want to train only one game, take Atari Alien using seed 10 for example

• Use PPO

python -m baselines.ppo2.run_atari --env AlienNoFrameskip-v4  --num-timesteps 10000000 --seed 10

• Use POP3D

python -m baselines.ppo2.run_atari --env AlienNoFrameskip-v4  --num-timesteps 10000000 --seed 10 --use-penal 1

Results

You can download results on three seeds from google drive https://drive.google.com/file/d/1c79TqWn74mHXhLjoTWaBKfKaQOsfD2hg/view?usp=sharing. We release it to make reproduction of this paper easy.

Atari results

Mujoco results

Acknowledge

Thanks to OpenAI's baselines, our code is based on https://github.com/openai/baselines.git