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Deep Reinforcement Learning Project - Winterterm 21/22

How to run the code?

First, clone the repo

git clone https://github.com/Marco5de/DeepRL.git
cd DeepRL

I recommend installing all of the required modules in a fresh virtualenv.

virtualenv venv
source venv/bin/activate

then install most of the required modules directly using pip

pip install -r requirements.txt

install all of the external dependencies beginning with openAI gym

cd src
mkdir extern && cd extern
git clone https://github.com/openai/gym.git
cd gym
pip install -e .
cd ..

and pybullet-gym

git clone https://github.com/benelot/pybullet-gym.git
cd pybullet-gym
pip install -e .
cd ../../../

with that the installation of all required modules is complete.
Now to run a training run the train.py script which in turn is using the small lib implementing the PPO algorithm.

python3 src/train.py

Changing between environments and hyperparameters

Most of the settings for the training are contained in the train.py script as global variables. The following contains a brief description for each of the available settings

  • SAVE_MODEL_FREQ: model is saved every SAVE_MODEL_FREQ
  • LOG_FREQ: log to stdout every LOG_FREQ iterations, note that no logging lib is used but simple print output
  • TRAIN_STEPS: total number of training iterations, note that this does not directly correspond to the number of training steps often found in literature as each train_step corresponds to a total of N * T timesteps but also may depend on the environment sequence length
  • ENV_IDX: Specifies which environment is used, see ENV_NAMES for an enumeration of the respective environments
  • RENDER_ENV: Specifies if the environment is rendered during training, note that the pybulletgym rendering works different from the openai-gym rendering as the env.render() function must only be called once!

The hyperparameters can theoretically be read from a yaml in the same format as shown in link. Note that not all options are currently implemented and the read_yaml() is currently unused! The simplest way is to manually adjust the default initialization in the ctor of the class. The following lists the hyperparameters for the environments that were considered in the report.

Hyperparameter Pendulum-v0 AntPyBulletEnv-v0
epsilon_clip 0.2 0.2
gamma 0.99 0.99
beta 1.0 1.0
d_target_ratio 1.5 1.5
d_target 0.25 0.25
var 0.5 0.1
N 2048 2048
T 200 32
K 10 10
numeric_stable 1e-10 1e-10
base_lr 3e-4 2.5e-4

Logging with Tensorboard

The implementation is using tensorboard to log the loss function values of the policy and value function network. Additionally, the processing time, average episode length and average episodic reward are all logged. The default output location is src/res/log_dir. To visualize the data in tensorboard simply start a server with the log directory

tensorboard --port 6006 --logdir srd/res/log_dir

Saved models

The default path for model is src/res/model each model consists of the two MLP checkpoints policy_net.pth and value_net.pth respectively which are simply saved and loaded using the PyTorch library. Additionally, the normalization of the environment must be preserved which is why a vec_normalize.pkl is also present. To save and load a model simply use the functionality provided in the PPO.py class: load_model(), save_model().

Compile report from LaTeX

The report including all resources can be found in the report directory. The LaTeX code is compiled using latexmk, thus it is assumed that it is installed alongside all required LaTeX packages. All of them should be contained in a full texlive installation. The easiest way to build the report is using the provided Makefile. To clean up the generated build files use make clean.