DreamerV3 Repo Skill

Use this skill when a task involves DreamerV3, the dreamer Python distribution, its dreamerv3 training entry point, or the bundled embodied RL runtime. The repository implements DreamerV3 world-model reinforcement learning with a fixed-hyperparameter training CLI, JAX/Ninjax model internals, environment adapters, replay/dataflow utilities, and result/log plotting helpers.

Start here

1. Check that the installed package is importable and the intended JAX backend is visible:

   python scripts/check_dreamerv3_install.py --backend cpu
   python scripts/check_dreamerv3_install.py --backend auto --json
   

2. Pick the closest route below. Read only the sub-skill needed for the task, then follow its linked references and bundled scripts.

3. If the current checkout or package version may differ from this skill, read references/repo-provenance.md before relying on version-sensitive details.

4. For cross-cutting setup or failure triage, read references/troubleshooting.md.

Route map

User task	Read this
Compose a training command, choose --configs, run a bounded debug smoke, resume/evaluate a checkpoint, or pick train, train_eval, eval_only, or parallel	sub-skills/train-configure/SKILL.md
Implement or debug an embodied.Env, Driver, replay buffer, stream, wrapper chain, built-in env adapter, or custom dataflow contract	sub-skills/embodied-dataflow/SKILL.md
Inspect or modify dreamerv3.agent.Agent, RSSM, encoder/decoder, JAX setup, Ninjax state, heads/outputs, losses, optimizers, sharding, dtype, or numerics	sub-skills/jax-models/SKILL.md
Install DreamerV3, reason about CUDA/Docker/optional suite dependencies, inspect logdirs, summarize metrics/scores, use Scope/TensorBoard/W&B, or triage operational failures	sub-skills/results-ops/SKILL.md

Minimal install and import facts

• Public repository: danijar/dreamerv3.
• Python requirement from README: Python 3.11+.
• Distribution name from package metadata: dreamer.
• Import roots: dreamerv3 and embodied.
• Package version captured for this skill: 3.3.1.
• Base runtime requirements include JAX CUDA 12 packages, elements, ninjax, optax, portal, scope, ale_py, autorom, av, and NumPy <2.
• Several environment suites are optional in practice: DMC/Loconav need dm_control; Crafter needs crafter; DMLab needs deepmind_lab; Minecraft needs MineRL and Java; ProcGen needs procgen; Gym/Memory Maze need their respective packages.

A minimal import check should succeed before any larger run:

import dreamerv3
import embodied
from embodied.envs import dummy

For JAX-only debugging, prefer an explicit CPU platform first:

JAX_PLATFORM_NAME=cpu python scripts/check_dreamerv3_install.py --backend cpu

Common operating decisions

• Use debug and dummy_disc for fast installation, config, replay, logging, and JAX sanity checks. Do not expect learning quality from the debug preset.
• Use real task presets such as crafter, atari, dmc_vision, dmc_proprio, dmlab, minecraft, or procgen only after optional suite dependencies are installed and the backend is appropriate.
• Reuse a --logdir only when continuing a compatible run. Changing size presets, RSSM dimensions, head bins, action/observation spaces, or major model config usually requires a fresh logdir or selective checkpoint restore.
• Treat CUDA as a production-performance backend, not a substitute for fixing a failing CPU debug smoke. First make the debug CPU route work, then scale up.
• Original repository tests and examples are verification evidence. Runtime workflows in this generated skill use bundled references/scripts instead of requiring future agents to open source files from a checkout.

Bundled root references and scripts

• references/repo-provenance.md: source commit, package version, dirty-state baseline, and evidence paths.
• references/troubleshooting.md: cross-cutting install/import/JAX/config/task/logdir triage and sub-skill routing.
• references/repo-routing-metadata.json: structured scenario placement for managed router import.
• scripts/check_dreamerv3_install.py: safe package/JAX/config/dummy-env smoke checker.

Do not use this skill for

• Generic RL theory questions that do not involve DreamerV3, world models, or the embodied runtime.
• Editing an unrelated reinforcement-learning library such as Stable-Baselines3, CleanRL, Tianshou, PettingZoo, or Gymnasium.
• Full benchmark reproduction claims without running the required environment, seed, hardware, and duration-specific experiments.
• Tasks that require private datasets, credentials, cloud accounts, or long training jobs without explicit user approval.