Update 14/05/2021: Added PyTorch implementation of REINFORCE.
Update 11/05/2021: Added PyTorch implementation of SAC.
Update 13/04/2021: Converted DDPG to Tensorflow 2.
Different algorithms have currently been implemented (in no particular order):
- Advantage Actor Critic
- Asynchronous Advantage Actor Critic (A3C)
- Deep Deterministic Policy Gradient (DDPG)
- Proximal Policy Optimization (PPO)
- Soft Actor-Critic (SAC) (TF2, PyTorch)
- Trust Region Policy Optimization (TRPO)
- REINFORCE (TF2, PyTorch) (convolutional neural network part has not been tested yet)
- Cross-Entropy Method
- Q-Learning
- Deep Q-Learning
- Fitted Q Iteration
- Sarsa with with function approximation and eligibility traces
- (Sequential) knowledge transfer
- Asynchronous knowledge transfer
The code for this algorithm can be found here. Example run after training using 16 threads for a total of 5 million timesteps on the PongDeterministic-v4 environment:
First, install the library using pip (you can first remove OpenCV from the setup.py file if it is already installed):
pip install yarll
To use the library on a specific branch or to use it while changing the code, you can add the path to the library to your $PYTHONPATH (e.g., in your .bashrc or .zshrc file):
export PYTHONPATH=/path/to/yarll:$PYTHONPATH
Alternatively, you can add a symlink from your site-packages to the yarll directory.
You can run algorithms by passing the path to an experiment specification (which is a file in json format) to main.py:
python yarll/main.py <path_to_experiment_specification>
You can see all the possible arguments by running python yarll/main.py -h.
Examples of experiment specifications can be found in the experiment_specs folder.
Statistics can be plot using:
python -m yarll.misc.plot_statistics <path_to_stats>
<path_to_stats> can be one of 2 things:
- A json file generated using
gym.wrappers.Monitor, in case it plots the episode lengths and total reward per episode. - A directory containing TensorFlow scalar summaries for different tasks, in which case all of the found scalars are plot.
Help about other arguments (e.g. for using smoothing) can be found by executing python -m yarll.misc.plot_statistics -h.
Alternatively, it is also possible to use Tensorboard to show statistics in the browser by passing the directory with the scalar summaries as --logdir argument.