Soft Actor-Critic

Soft actor-critic is a deep reinforcement learning framework for training maximum entropy policies in continous domains. The algorithm is based on the paper Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor presented at the Deep Reinforcement Learning Symposium, NIPS 2017.

This implementation uses Tensorflow. For a PyTorch implementation of soft actor-critic, take a look at rlkit by Vitchyr Pong.

See the DIAYN documentation for using SAC for learning diverse skills.

Getting Started

Soft Actor-Critic can be run either locally or through Docker.

Prerequisites

You will need to have Docker and Docker Compose installed unless you want to run the environment locally.

Most of the models require a Mujoco license.

Docker installation

If you want to run the Mujoco environments, the docker environment needs to know where to find your Mujoco license key (mjkey.txt). You can either copy your key into <PATH_TO_THIS_REPOSITY>/.mujoco/mjkey.txt, or you can specify the path to the key in your environment variables:

export MUJOCO_LICENSE_PATH=<path_to_mujoco>/mjkey.txt

Once that's done, you can run the Docker container with

docker-compose up

Docker compose creates a Docker container named soft-actor-critic and automatically sets the needed environment variables and volumes.

You can access the container with the typical Docker exec-command, i.e.

docker exec -it soft-actor-critic bash

See examples section for examples of how to train and simulate the agents.

To clean up the setup:

docker-compose down

Local installation

To get the environment installed correctly, you will first need to clone rllab, and have its path added to your PYTHONPATH environment variable.

1. Clone rllab

cd <installation_path_of_your_choice>
git clone https://github.com/rll/rllab.git
cd rllab
git checkout b3a28992eca103cab3cb58363dd7a4bb07f250a0
export PYTHONPATH=$(pwd):${PYTHONPATH}

2. Download and copy mujoco files to rllab path: If you're running on OSX, download https://www.roboti.us/download/mjpro131_osx.zip instead, and copy the .dylib files instead of .so files.

mkdir -p /tmp/mujoco_tmp && cd /tmp/mujoco_tmp
wget -P . https://www.roboti.us/download/mjpro131_linux.zip
unzip mjpro131_linux.zip
mkdir <installation_path_of_your_choice>/rllab/vendor/mujoco
cp ./mjpro131/bin/libmujoco131.so <installation_path_of_your_choice>/rllab/vendor/mujoco
cp ./mjpro131/bin/libglfw.so.3 <installation_path_of_your_choice>/rllab/vendor/mujoco
cd ..
rm -rf /tmp/mujoco_tmp

3. Copy your Mujoco license key (mjkey.txt) to rllab path:

cp <mujoco_key_folder>/mjkey.txt <installation_path_of_your_choice>/rllab/vendor/mujoco

4. Clone sac

cd <installation_path_of_your_choice>
git clone https://github.com/haarnoja/sac.git
cd sac

5. Create and activate conda environment

cd sac
conda env create -f environment.yml
source activate sac

The environment should be ready to run. See examples section for examples of how to train and simulate the agents.

Finally, to deactivate and remove the conda environment:

source deactivate
conda remove --name sac --all

Examples

Training and simulating an agent

1. To train the agent

python ./examples/mujoco_all_sac.py --env=swimmer --log_dir="/root/sac/data/swimmer-experiment"

2. To simulate the agent (NOTE: This step currently fails with the Docker installation, due to missing display.)

python ./scripts/sim_policy.py /root/sac/data/swimmer-experiment/itr_<iteration>.pkl

mujoco_all_sac.py contains several different environments and there are more example scripts available in the /examples folder. For more information about the agents and configurations, run the scripts with --help flag. For example:

python ./examples/mujoco_all_sac.py --help
usage: mujoco_all_sac.py [-h]
                         [--env {ant,walker,swimmer,half-cheetah,humanoid,hopper}]
                         [--exp_name EXP_NAME] [--mode MODE]
                         [--log_dir LOG_DIR]

Credits

The soft actor-critic algorithm was developed by Tuomas Haarnoja under the supervision of Prof. Sergey Levine and Prof. Pieter Abbeel at UC Berkeley. Special thanks to Vitchyr Pong, who wrote some parts of the code, and Kristian Hartikainen who helped testing, documenting, and polishing the code and streamlining the installation process. The work was supported by Berkeley Deep Drive.

Reference

@article{haarnoja2017soft,
  title={Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor},
  author={Haarnoja, Tuomas and Zhou, Aurick and Abbeel, Pieter and Levine, Sergey},
  booktitle={Deep Reinforcement Learning Symposium},
  year={2017}
}