This repo implements the Dreamer algorithm from Dream to Control: Learning Behaviors By latent Imagination based on the PlaNet-Pytorch. It has been confirmed working on the DeepMind Control Suite/MuJoCo environment. Hyperparameters have been taken from the paper.

To install all dependencies with Anaconda run using the following commands.

conda env create -f conda_env.yml

source activate dreamer

python main.py --algo dreamer --env walker-walk --action-repeat 2 --id name-of-experiement

For best performance with DeepMind Control Suite, try setting environment variable MUJOCO_GL=egl (see instructions and details here).

Use Tensorboard to monitor the training.

tensorboard --logdir results

The performances are compared with the other SoTA algorithms as follows (Note! Tested once using seed 0.)

• State-SAC
• PlaNet-PyTorch
• SAC-AE
• SLAC
• CURL
• Dreamer (tensorflow2 implementation)

Pretrained models can be found in the releases.

• Dream to Control: Learning Behaviors By latent Imagination
• google-research/dreamer