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name: tensorforce description: "Guide Tensorforce reinforcement-learning package workflows, including agents, environments, Runner execution, configuration modules, persistence, export, and troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Tensorforce

Use this repo skill when a task involves the Tensorforce Python package: creating reinforcement-learning agents, defining Tensorforce environments, configuring networks/objectives/optimizers, running Runner training/evaluation, saving/loading/exporting agents, or diagnosing Tensorforce 0.6.x dependency and API issues.

Tensorforce is an older TensorFlow-based RL framework. Prefer bounded CPU smoke checks before long training, and treat optional simulator, GPU, TensorFlow Addons, and BOHB tuning integrations as opt-in capabilities that require their own dependencies and evidence.

Start here

1. Read repo provenance before judging staleness for a checkout or package version.
2. Read installation and inspection before installing, importing, or repairing dependencies.
3. Run or adapt scripts/check_tensorforce_env.py to verify an installed Tensorforce runtime.
4. Route the user task to the narrowest sub-skill below.
5. Use cross-cutting troubleshooting for dependency, TensorFlow, Gym, optional extra, and package-age failures.

Route by task

User task	Read
Create/load agents, choose algorithm aliases, specify states/actions, use action masks, write act/observe or experience/update loops	agents-and-specifications
Configure networks, layers, preprocessing, memories, policies, objectives, optimizers, parameters, JSON/dict module specs, and config fields	modules-and-configuration
Implement or wrap environments, use Environment.create, Gym/custom adapters, reward shaping, vectorized/multi-actor/remote interaction, optional simulator adapters	environments-and-interaction
Run bounded training/evaluation with Runner, translate historical run.py flags, configure callbacks/logging/parallelism, reason about optional BOHB tuning	runner-and-cli-workflows
Save/load agents, checkpoints, summaries/tracking, recorder/pretraining, and TensorFlow SavedModel export	persistence-export-and-recording

Minimal import check

python - <<'PY'
import tensorforce
from tensorforce import Agent, Environment, Runner
print(tensorforce.__version__)
print(Agent.create, Environment.create, Runner)
PY

For a stronger smoke check from this skill directory:

python scripts/check_tensorforce_env.py --smoke-agent

Operating constraints

• Do not assume historical repository-level scripts, examples, benchmark configs, or tests exist in the user's project. Use the bundled references and scripts in this skill.
• Do not claim CARLA, ALE, Retro, ViZDoom, OpenSim/PLE, TensorFlow Addons, BOHB tuning, or GPU execution is verified unless the user's runtime has the required extra and a bounded check passes.
• Keep training examples short by default. Long RL runs are task experiments, not installation checks.
• Tensorforce 0.6.x dependency pins are old. If modern pip resolution fails around TensorFlow/NumPy/Gym, use installation and inspection and document the exact compatible environment chosen.