AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation

Pytorch implementation of AR-DAE on our paper:

Jae Hyun Lim, Aaron Courville, Christopher Pal, Chin-Wei Huang, AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation (2020)

Toy example of AR-DAE

Example code to train AR-DAE on swiss roll dataset:
ipython-notebook

Energy function fitting with AR-DAE

Example code to train an implicit sampler using AR-DAE-based entropy gradient estimator:
ipython-notebook

AR-DAE VAE

please find the code at https://github.com/lim0606/pytorch-ardae-vae

SAC-AR-DAE

Getting Started

Requirements

python>=3.6
pytorch==1.4.0
tensorflow (for tensorboardX)
tensorboardX
git+https://github.com/lim0606/torchkit.git (for sac-nf)

Requirements (OpenAI gym)

mujoco
mujoco_py

• Install mujoco

  wget https://mujoco.org/download/mujoco200_linux.zip
  unzip mujoco200_linux.zip
  mkdir -p ~/.mujoco
  mv mujoco200_linux ~/.mujoco/mujoco200
  

  cp mjkey.txt ~/.mujoco/.
  

• Install mujoco_py

  git clone https://github.com/openai/mujoco-py.git
  cd mujoco-py
  pip install -e .
  

• Install gym

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

Requirements (Rllab)

mujoco131
git+https://github.com/inksci/mujoco-py-v0.5.7.git
git+https://github.com/Theano/Theano.git@adfe319ce6b781083d8dc3200fb4481b00853791#egg=Theano
git+https://github.com/openai/[email protected]#egg=gym
PyOpenGL
pyglet
rllab

• Install rllab

  git clone https://github.com/rll/rllab.git
  cd rllab
  pip install -e .
  

• Install mujoco

  wget https://mujoco.org/download/mujoco131_linux.zip
  unzip mujoco131_linux.zip
  mv mjpro131 <path-to-rllab>/vendor/mujoco
  

  cp mjkey.txt <path-to-rllab>/vendor/mujoco/.
  

Structure

• utils: miscelleneous functions
• models: model classes for ar-dae
• model.py model classes for rl experiments
• main_gs.py: main function to train model (sac)
• main_nf.py: main function to train model (sac-nf)
• main_ardae.py: main function to train model (sac-ar-dae)

Experiments

Train

• For example, you can train a SAC-AR-DAE for Ant-v2 environment as follows,

  python main_ardae.py --cuda \
  --cache experiments/ant --env-name Ant-v2 \
  --alpha 0.05 --start_steps 10000 \
  --noise_size 10 --policy_type mlp --policy_nonlin elu --num_enc_layers 1 --num_fc_layers 1 \
  --lmbd 100000 --nu 1.1 --eta 0.01 --num-pert-samples 10 --jac-act tanh \
  --gqnet_nonlin relu --gqnet_num_layers 1 \
  --dae-type grad --dae-nonlin elu --dae_num_layers 5 --dae-enc-ctx true --dae-ctx-type state \
  --train-nz-cdae 128 --train-nstd-cdae 1 --num-cdae-updates 1 \
  --std-scale 10000 --delta 0.1 \
  --d-optimizer adam --d-lr 0.0003 --d-beta1 0.9 --d-momentum 0.9 \
  --q-optimizer adam --lr 0.0003 --q-beta1 0.9 --q-momentum 0.9 \
  --mean-sub-method none --mean-upd-method avg --mean-sub-tau 0.005 --use-ptfnc 100 \
  --log-interval 1000 --eval-interval 10000 --ckpt-interval 20000 --seed -1 --exp-num 1

  For more information, please find example scripts, run.sh.

Contact

For questions and comments, feel free to contact Jae Hyun Lim and Chin-Wei Huang.

License

MIT License

Reference

@article{jaehyun2020ardae,
  title={{AR-DAE}: Towards Unbiased Neural Entropy Gradient Estimation},
  author={Jae Hyun Lim and
          Aaron Courville and
          Christopher J. Pal and
          Chin-Wei Huang},
  journal={arXiv preprint arXiv:2006.05164},
  year={2020}
}