letusfly85 / slac

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

Stochastic Latent Actor-Critic

[Project Page] [Paper]

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model,
Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine.
arXiv preprint arXiv:1907.00953, 2019.

Getting started

Prerequisites

• Linux or macOS
• Python >=3.5
• CPU or NVIDIA GPU + CUDA CuDNN

Installation

• Clone this repo:

git clone -b master --single-branch https://github.com/alexlee-gk/slac.git
cd slac

• To use the DeepMind Control Suite, follow the instructions in the dm_control package.
• To use OpenAI Gym , follow the instructions in the gym and mujoco_py packages.
• Modify the requirements.txt file if necessary:
  • Replace tf-nightly-gpu with tf-nightly if using CPU.
  • Omit gym, mujoco-py, or dm_control accordingly if only using one of the suites.
• Install python packages:

pip install -r requirements.txt

• Install the tf_agents package:

pip install git+git://github.com/tensorflow/agents.git

• Install ffmpeg (optional, used to generate GIFs for visualization in TensorBoard).
• For some python installations, the root directory should be added to the PYTHONPATH:

export PYTHONPATH=path/to/slac:$PYTHONPATH

Examples usage

CUDA_VISIBLE_DEVICES=0 python slac/agents/slac/examples/v1/train_eval.py \
  --root_dir logs \
  --experiment_name slac \
  --gin_file slac/agents/slac/configs/slac.gin \
  --gin_file slac/agents/slac/configs/dm_control_cheetah_run.gin

To view training and evaluation information (e.g. learning curves, GIFs of rollouts and predictions), run tensorboard --logdir logs and open http://localhost:6006.

The gin-configurable parameters can be modified using the --gin_param flag, e.g.

CUDA_VISIBLE_DEVICES=0 python slac/agents/slac/examples/v1/train_eval.py \
  --root_dir logs \
  --experiment_name slac \
  --gin_file slac/agents/slac/configs/slac.gin \
  --gin_file slac/agents/slac/configs/dm_control_cheetah_run.gin \
  --gin_param train_eval.gpu_allow_growth=True \
  --gin_param train_eval.sequence_length=8 \
  --gin_param train_eval.action_repeat=2

Troubleshooting

No matching distribution found for tf-nightly-gpu==1.15.0.dev20190821 (or similar) when installing packages in requirements.txt.

Upgrade pip: pip install --upgrade pip.

pkg_resources.VersionConflict: (setuptools 40.8.0 (.../lib/python3.7/site-packages), Requirement.parse('setuptools>=41.0.0')) when running tensorboard.

Upgrade setuptools: pip install --upgrade setuptools.

Other errors

Make sure to exactly use the versions of the python packages in the requirements.txt file and in the installation instructions, e.g.

pip install --upgrade -r requirements.txt
pip install --upgrade git+git://github.com/tensorflow/agents.git

Citation

If you find this useful for your research, please use the following.

@article{lee2019slac,
  title={Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model},
  author={Alex X. Lee and Anusha Nagabandi and Pieter Abbeel and Sergey Levine},
  journal={arXiv preprint arXiv:1907.00953},
  year={2019}
}