A reimplementation of DreamerV3, a scalable and general reinforcement learning algorithm that masters a wide range of applications with fixed hyperparameters.

If you find this code useful, please reference in your paper:

@article{hafner2023dreamerv3,
  title={Mastering Diverse Domains through World Models},
  author={Hafner, Danijar and Pasukonis, Jurgis and Ba, Jimmy and Lillicrap, Timothy},
  journal={arXiv preprint arXiv:2301.04104},
  year={2023}
}

To learn more:

• Research paper
• Project website
• Twitter summary

DreamerV3 learns a world model from experiences and uses it to train an actor critic policy from imagined trajectories. The world model encodes sensory inputs into categorical representations and predicts future representations and rewards given actions.

DreamerV3 masters a wide range of domains with a fixed set of hyperparameters, outperforming specialized methods. Removing the need for tuning reduces the amount of expert knowledge and computational resources needed to apply reinforcement learning.

Due to its robustness, DreamerV3 shows favorable scaling properties. Notably, using larger models consistently increases not only its final performance but also its data-efficiency. Increasing the number of gradient steps further increases data efficiency.

If you just want to run DreamerV3 on a custom environment, you can pip install dreamerv3 and copy example.py from this repository as a starting point.

If you want to make modifications to the code, you can either use the provided Dockerfile that contains instructions or follow the manual instructions below.

Install JAX and then the other dependencies:

pip install -r requirements.txt

Simple training script:

python example.py

Flexible training script:

python dreamerv3/train.py \
  --logdir ~/logdir/$(date "+%Y%m%d-%H%M%S") \
  --configs crafter --batch_size 16 --run.train_ratio 32

• All config options are listed in configs.yaml and you can override them from the command line.
• The debug config block reduces the network size, batch size, duration between logs, and so on for fast debugging (but does not learn a good model).
• By default, the code tries to run on GPU. You can switch to CPU or TPU using the --jax.platform cpu flag. Note that multi-GPU support is untested.
• You can run with multiple config blocks that will override defaults in the order they are specified, for example --configs crafter large.
• By default, metrics are printed to the terminal, appended to a JSON lines file, and written as TensorBoard summaries. Other outputs like WandB can be enabled in the training script.
• If you get a Too many leaves for PyTreeDef error, it means you're reloading a checkpoint that is not compatible with the current config. This often happens when reusing an old logdir by accident.
• If you are getting CUDA errors, scroll up because the cause is often just an error that happened earlier, such as out of memory or incompatible JAX and CUDA versions.
• You can use the small, medium, large config blocks to reduce memory requirements. The default is xlarge. See the scaling graph above to see how this affects performance.
• Many environments are included, some of which require installating additional packages. See the installation scripts in scripts and the Dockerfile for reference.
• When running on custom environments, make sure to specify the observation keys the agent should be using via encoder.mlp_keys, encode.cnn_keys, decoder.mlp_keys and decoder.cnn_keys.
• To log metrics from environments without showing them to the agent or storing them in the replay buffer, return them as observation keys with log_ prefix and enable logging via the run.log_keys_... options.
• To continue stopped training runs, simply run the same command line again and make sure that the --logdir points to the same directory.

This repository contains a reimplementation of DreamerV3 based on the open source DreamerV2 code base. It is unrelated to Google or DeepMind. The implementation has been tested to reproduce the official results on a range of environments.