---

name: environment-utils description: "Use PARL environment wrappers, replay buffers, schedulers, CSV logging, and summary helpers safely across Gym, continuous-control, Atari, MuJoCo, multi-agent, and vector-env workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

PARL environment utilities

Use this sub-skill when a task involves PARL's environment wrappers or small utilities around an RL loop: Gym API compatibility, continuous action remapping, Atari/MuJoCo/multi-agent/vector environment wrappers, ReplayMemory, schedulers, CSVLogger, logger, summary, tensorboard, or visualdl helpers.

Do not use this sub-skill to choose a full algorithm, implement a parl.Model/parl.Agent, or manage xparl workers. Route adjacent tasks to sibling operating skills:

• ../core-framework/SKILL.md for backend selection, Model/Algorithm/Agent, save/restore, inference export, and weight synchronization.
• ../algorithm-recipes/SKILL.md for DQN/DDPG/TD3/SAC/PPO/QMIX/MADDPG/A2C/IMPALA/CQL/OAC recipes and training-loop structure.
• ../xparl-distributed/SKILL.md for @parl.remote_class, parl.connect, xparl start, remote environments, ports, and cluster security.

Operating workflow

1. Normalize the environment API first. For ordinary Gym environments, wrap with CompatWrapper before calling code that expects the old four-return step API. Then add workflow-specific wrappers such as ActionMappingWrapper, wrap_deepmind, wrap_rms, MAenv, or VectorEnv.

2. Choose wrappers by action/observation surface. Use ActionMappingWrapper only when the environment action space is a continuous Box; use Atari wrappers only for Atari-like image/action-meaning environments; use MuJoCo RMS wrappers for continuous-control normalization; use multi-agent wrappers only when the PettingZoo or legacy multiagent dependency stack is intentionally installed.

3. Check data utility shapes before training. Configure ReplayMemory(max_size, obs_dim, act_dim) from the actual observation and action dimensions. For discrete actions pass act_dim=0; for continuous actions pass a positive integer action dimension.

4. Keep logging output explicit. Set a deliberate logger.set_dir(...) before writing summaries, or create CSVLogger with an explicit file path in a scratch/output directory. Avoid accidental deletion from logger helpers when a directory already exists.

5. Smoke-test utilities safely. Run the bundled checker before adapting examples:

   python scripts/check_env_utils.py --json
   

   Add --optional-wrappers to classify optional Atari, MuJoCo, and multi-agent imports without creating real environments.

6. Escalate by symptom. For wrapper/data-flow behavior, read references/wrappers-and-data.md; for logging and schedulers, read references/logging-and-schedulers.md; for failures, read references/troubleshooting.md.

Verification status

The utility signatures for CompatWrapper, ActionMappingWrapper, VectorEnv, ReplayMemory, PiecewiseScheduler, LinearDecayScheduler, and CSVLogger were checked against an installed PARL 2.2.1 package. Small scheduler, replay-memory, CSV, vector-env, and action-mapping behaviors are covered by the bundled checker. Atari, MuJoCo, PettingZoo multi-agent, TensorBoard, and VisualDL flows are optional dependency surfaces and should be rechecked in the user's target environment before use.

Reference map

• references/wrappers-and-data.md — Gym compatibility, action mapping, vector envs, Atari/MuJoCo/multi-agent wrappers, replay-memory contracts, and small NumPy RL helpers.
• references/logging-and-schedulers.md — schedulers, CSV output, PARL logger directories, summary/TensorBoard/VisualDL behavior, and file-write safety.
• references/troubleshooting.md — common fixes for Gym API mismatches, optional dependencies, action/replay shapes, logger deletion, CSV key mismatches, and summary backends.
• scripts/check_env_utils.py — deterministic local checker for safe imports, signatures, and tiny pure-Python utility exercises.