---

name: acme description: "Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Acme Repo Skill

Use this repo skill when a user asks about DeepMind Acme (dm-acme), Acme-style reinforcement-learning agents, dm_env loops, Reverb adders, Acme JAX experiments, or Acme TensorFlow/Sonnet agents.

Start Here

• Read references/repo-provenance.md before deciding whether this skill matches a current checkout or should be refreshed.
• Read references/package-overview.md for install extras, public dependency groups, repository capability map, and optional-backend expectations.
• Read references/troubleshooting.md for cross-cutting install/import, optional dependency, backend, and example-runtime failures.
• Run python scripts/check_acme_skill_runtime.py --help for a bundled, dependency-light helper that explains package import checks and available sub-skill helper scripts.

Route By Task

• Core loops/specs/wrappers: use sub-skills/core-workflows/SKILL.md for dm_env specs, Actor/Learner, EnvironmentLoop, wrappers, loggers, counters, observers, and simple custom loop debugging.
• Replay and data: use sub-skills/replay-and-data/SKILL.md for Adder contracts, Reverb adders, replay tables, dataset iterators, TFDS/offline data, and transition/sequence/episode shape decisions.
• JAX agents: use sub-skills/jax-agents/SKILL.md for Acme JAX algorithms, acme.jax.experiments, Haiku/Optax/RLax networks, online/offline/distributed JAX workflows, and JAX multiagent examples.
• TensorFlow agents: use sub-skills/tf-agents/SKILL.md for Acme TensorFlow/Sonnet algorithms, TF losses, TF savers/snapshotters, Launchpad TF examples, and TF learner debugging.

Install Baseline

• Distribution name: dm-acme; import package: acme; snapshot version: 0.4.1.
• Minimal package metadata lists core requirements: absl-py, dm-env, dm-tree, numpy, pillow, and typing-extensions.
• Real agent execution usually needs optional extras: dm-acme[jax] for JAX agents, dm-acme[tf] for TensorFlow/Reverb/Launchpad agents, and dm-acme[envs] for example environments.
• Treat optional extras as backend/runtime choices, not as always-required imports. Many examples require Gym, bsuite, Control Suite, Atari ROMs, OpenSpiel, Reverb servers, Launchpad launch modes, or long training budgets.

Minimal Import Check

python - <<'PY'
import acme
from acme import specs
print(acme.__version__)
print(specs.Array((2,), float))
PY

If this fails with ModuleNotFoundError for jax, tensorflow, reverb, launchpad, gym, bsuite, or environment packages, read references/troubleshooting.md before broadening installs.

Skill Boundaries

• This skill is for using, adapting, troubleshooting, and explaining Acme as an RL framework; it is not a benchmark reproduction plan and does not verify long training results.
• Do not route generic RL theory questions here unless the task names Acme APIs, Acme examples, or Acme-style builders/adders/loops.
• Do not tell future agents to open or run original repository files. This skill distills the repo evidence into bundled references and scripts.
• For source drift, compare current commit, dirty state, package metadata, and evidence paths with references/repo-provenance.md, then run refresh-repo-skill if they differ.