DreamerV2

Use this skill for the danijar/dreamerv2 2.2.0 package: discrete world-model reinforcement learning with TensorFlow, native Atari/DM Control/Crafter runners, custom legacy Gym environments, typed configuration, replay/checkpoints, and experiment plots. Treat this as a TensorFlow 2.6-era package, not a current Gymnasium or modern-Keras package.

Route the task

• Launch, bound, resume, or diagnose a run: read training.
• Adapt an environment or validate observations/actions: read environments.
• Compose presets, flags, schedules, or serialized configs: read configuration.
• Inspect metrics, TensorBoard artifacts, or compare runs: read evaluation.

Read only the route needed for the request, then follow its linked references. The sub-skills are one operating graph: a training request commonly visits configuration first, environments for the data contract, and evaluation after artifacts exist.

Install a compatible runtime

The verified package facts are distribution dreamerv2==2.2.0, import modules dreamerv2 and dreamerv2.api, and console entry point dreamerv2. This source release uses tensorflow.keras.mixed_precision.experimental and the legacy Gym API. Prefer a fresh Python 3.9 environment with a coherent era stack:

python -m pip install \
  'tensorflow==2.6.0' 'tensorflow-probability==0.14.1' \
  'gym==0.23.1' 'ruamel.yaml<0.18' \
  'numpy==1.19.5' 'pillow<10' 'pandas<2' 'matplotlib<3.8'
python -m pip install --no-deps 'dreamerv2==2.2.0'

Add only the suites you will use: atari_py plus imported Atari ROMs for Atari, a compatible dm_control/MuJoCo/EGL setup for DMC, crafter for Crafter, and the environment's own package for custom Gym. Do not download ROMs, MuJoCo assets, or credentials as an unattended smoke check. Read troubleshooting before mixing newer TensorFlow, TensorFlow Probability, Gym/Gymnasium, or NumPy versions.

Check the installation without starting training:

python -m pip check
python - <<'PY'
import inspect
import tensorflow as tf
import dreamerv2.api as dv2
print('TensorFlow', tf.__version__)
print('GPUs', tf.config.list_physical_devices('GPU'))
print('api.train', inspect.signature(dv2.train))
print('default task', dv2.defaults.task)
PY
python -m dreamerv2.train --help

The module help route is verified. Do not use the installed dreamerv2 launcher in this release: train.py derives configs.yaml from sys.argv[0], so the launcher may search beside its bin script and fail with a missing-config error. Use python -m dreamerv2.train instead. The plotting route is available through the evaluation sub-skill's bundled adapter.

Operating rules

• Native python -m dreamerv2.train asserts that TensorFlow sees a GPU; a CPU import or debug preset does not make native training CPU-safe.
• Use a new writable logdir per run. The effective config.yaml, replay episodes, metrics.jsonl, TensorBoard events, and variables.pkl are the observable outputs; a checkpoint alone is not a successful experiment.
• Validate one environment reset/step and its action/observation contract before allocating training. Keep --envs_parallel none for this snapshot unless the asynchronous branch has been patched and tested.
• Make debug the last preset, set a bounded --steps, and explicitly choose --precision 32 when the legacy mixed-precision path is not verified.
• Preserve exact package/runtime versions and effective config with results. For refresh decisions, read repository provenance.

Scope and limits

This graph distills public package behavior and safe operational recipes. It does not run benchmark-scale training, fetch external assets, import the skill into the managed repo library, or claim that a modern unpinned dependency set is compatible. Use cross-cutting troubleshooting for install/import, backend, dependency, and source-version failures before escalating to the focused route.