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name: environments-and-interaction description: "Create and troubleshoot Tensorforce environments, custom Environment subclasses, adapters, reward shaping, and parallel interaction boundaries." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Tensorforce environments and interaction

Use this sub-skill when the task is about constructing, implementing, wrapping, or debugging Tensorforce environments. It covers Environment.create(...), custom Environment subclasses, Gym/custom adapters, reward shaping, max_episode_timesteps and abort terminals, optional simulator adapters, vectorized/multi-actor environment contracts, and remote environment boundaries.

Route quickly

• Need the public environment factory forms or custom subclass contract? Read references/environment-api.md.
• Need Gym, custom_cartpole, ALE, Retro, ViZDoom, OpenSim/PLE, or CARLA adapter guidance? Read references/optional-environment-adapters.md.
• Need vectorized, multi-actor, multiprocessing, or socket environment boundaries? Read references/advanced-environments.md.
• Need to diagnose shape/type/terminal/socket/Gym dependency failures? Read references/troubleshooting.md.
• Need a small runtime check of custom environment behavior, reward shaping, and time-limit aborts? Run scripts/custom_environment_smoke.py --help, then run it in the user's Tensorforce environment.

Boundaries

This sub-skill owns environment-side behavior only. Route agent construction, act/observe, action masking policy behavior, and offline experience/update details to the agent interaction sub-skill. Route Runner.run(...), CLI training/evaluation, and tuning workflows to the runner workflow sub-skill. Route installation-wide dependency setup and TensorFlow device configuration to the root Tensorforce skill.

Optional simulator adapters are documented as public surfaces but were not executed during skill construction. Do not claim CARLA, ALE, Retro, ViZDoom, OpenSim, or PLE execution is verified unless the user supplies those dependencies/assets and you run a bounded adapter-specific smoke.

Safe workflow

1. Create environments through Environment.create(...) where possible, so Tensorforce can wrap max-timestep and reward-shaping behavior consistently.
2. For custom environments, implement the minimal subclass contract: states(), actions(), reset(), execute(actions), optional natural max_episode_timesteps(), and close().
3. Always decide who owns the episode length. If the environment has no natural episode limit, pass max_episode_timesteps to Environment.create(...) or Runner(...) so Tensorforce can emit terminal=2 for time-limit aborts.
4. Treat optional adapters and remote/socket workflows as dependency- and process-boundary work. Prefer bounded local smoke first, then add external simulator/server checks only when explicitly available.