Policy Gradient (PG) Algorithms

This repository contains PyTorch (v0.4.0) implementations of typical policy gradient (PG) algorithms.

Vanilla Policy Gradient [1]
Truncated Natural Policy Gradient [4]
Trust Region Policy Optimization [5]
Proximal Policy Optimization [7].

We have implemented and trained the agents with the PG algorithms using the following benchmarks. Trained agents and Unity ml-agent environment source files will soon be available in our repo!

mujoco-py: https://github.com/openai/mujoco-py
Unity ml-agent: https://github.com/Unity-Technologies/ml-agents

For reference, solid reviews of below papers related to PG (in Korean) are located in https://reinforcement-learning-kr.github.io/2018/06/29/0_pg-travel-guide/. Enjoy!

[1] R. Sutton, et al., "Policy Gradient Methods for Reinforcement Learning with Function Approximation", NIPS 2000.
[2] D. Silver, et al., "Deterministic Policy Gradient Algorithms", ICML 2014.
[3] T. Lillicrap, et al., "Continuous Control with Deep Reinforcement Learning", ICLR 2016.
[4] S. Kakade, "A Natural Policy Gradient", NIPS 2002.
[5] J. Schulman, et al., "Trust Region Policy Optimization", ICML 2015.
[6] J. Schulman, et al., "High-Dimensional Continuous Control using Generalized Advantage Estimation", ICLR 2016.
[7] J. Schulman, et al., "Proximal Policy Optimization Algorithms", arXiv, https://arxiv.org/pdf/1707.06347.pdf.

Table of Contents

Policy Gradient (PG) Algorithms

Mujoco-py

1. Installation

Ubuntu

2. Train

Navigate to pg_travel/mujoco folder

Basic Usage

Train the agent with PPO using Hopper-v2 without rendering.

python main.py

Note that models are saved in save_model folder automatically for every 100th iteration.

Train the agent with TRPO using HalfCheetah with rendering

python main.py --algorithm TRPO --env HalfCheetah-v2 --render

algorithm: PG, TNPG, TRPO, PPO(default)
env: Ant-v2, HalfCheetah-v2, Hopper-v2(default), Humanoid-v2, HumanoidStandup-v2, InvertedPendulum-v2, Reacher-v2, Swimmer-v2, Walker2d-v2

Continue training from the saved checkpoint

python main.py --load_model ckpt_736.pth.tar

Note that ckpt_736.pth.tar file should be in the pg_travel/mujoco/save_model folder.
Pass the arguments algorithm and/or env if not PPO and/or Hopper-v2.

Test the pretrained model

Play 5 episodes with the saved model ckpt_738.pth.tar

python test_algo.py --load_model ckpt_736.pth.tar --iter 5

Note that ckpt_736.pth.tar file should be in the pg_travel/mujoco/save_model folder.
Pass the arguments env if not Hopper-v2.

Modify the hyperparameters

Hyperparameters are listed in hparams.py. Change the hyperparameters according to your preference.

3. Tensorboard

We have integrated TensorboardX to observe training progresses.

Note that the results of trainings are automatically saved in logs folder.
TensorboardX is the Tensorboard-like visualization tool for Pytorch.

Navigate to the pg_travel/mujoco folder

tensorboard --logdir logs

4. Trained Agent

We have trained the agents with four different PG algortihms using Hopper-v2 env.

Algorithm	Score	GIF
Vanilla PG
NPG
TRPO
PPO

Unity ml-agents

1. Installation

2. Environments

We have modified Walker environment provided by Unity ml-agents.

Overview	image
Walker
Plane Env
Curved Env

Description

212 continuous observation spaces
39 continuous action spaces
16 walker agents in both Plane and Curved envs
Reward
- +0.03 times body velocity in the goal direction.
- +0.01 times head y position.
- +0.01 times body direction alignment with goal direction.
- -0.01 times head velocity difference from body velocity.
- +1000 for reaching the target
Done
- When the body parts other than the right and left foots of the walker agent touch the ground or walls
- When the walker agent reaches the target

Prebuilt Unity envrionements

Contains Plane and Curved walker environments for Linux / Mac / Windows!
Linux headless envs are also provided for faster training and server-side training.
Download the corresponding environments, unzip, and put them in the pg_travel/unity/env folder.

3. Train

Navigate to the pg_travel/unity folder

Basic Usage

Train walker agent with PPO using Plane environment without rendering.

python main.py --train

The PPO implementation is for multi-agent training. Collecting experiences from multiple agents and using them for training the global policy and value networks (brain) are included. Refer to pg_travel/mujoco/agent/ppo_gae.py for just single-agent training.
See arguments in main.py. You can change hyper parameters for the ppo algorithm, network architecture, etc.
Note that models are saved in save_model folder automatically for every 100th iteration.

Continue training from the saved checkpoint

python main.py --load_model ckpt_736.pth.tar --train

Note that ckpt_736.pth.tar file should be in the pg_travel/unity/save_model folder.

Test the pretrained model

python main.py --render --load_model ckpt_736.pth.tar

Note that ckpt_736.pth.tar file should be in the pg_travel/unity/save_model folder.

Modify the hyperparameters

See main.py for default hyperparameter settings. Pass the hyperparameter arguments according to your preference.

4. Tensorboard

We have integrated TensorboardX to observe training progresses.

Navigate to the pg_travel/unity folder

tensorboard --logdir logs

5. Trained Agent

We have trained the agents with PPO using plane and curved envs.

Env	GIF
Plane
Curved

Reference

We referenced the codes from below repositories.