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name: algorithm-recipes description: "Select and adapt PARL built-in reinforcement-learning algorithms and safe recipe skeletons." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

PARL Algorithm Recipes

Use this sub-skill when a task asks which PARL algorithm to use, which parl.Model methods an algorithm expects, how to adapt a PARL example into a safe training/evaluation loop, or how to interpret PARL's TIPC and challenge examples without running unsafe launchers.

Route first

• Need parl.Model, parl.Agent, backend selection, save/restore, or weight-sync basics? Read ../core-framework/SKILL.md first.
• Need wrappers, replay buffers, schedulers, vector environments, logging, or Gym API compatibility? Read ../environment-utils/SKILL.md with this sub-skill.
• Need xparl start/connect, remote actors, distributed file shipping, ports, or cluster security? Read ../xparl-distributed/SKILL.md before adapting distributed examples.
• Need Waymax all-GPU autonomous-driving PPO or EvoKit C++ evolution-strategy workflows? Route to ../waymax-rl/SKILL.md or ../evo-kit/SKILL.md; this sub-skill only names them as optional specialized workflows.

Choose the reference

1. Read references/algorithm-catalog.md to select an algorithm family and confirm required model methods.
2. Read references/training-workflows.md for safe training/evaluation skeletons and QuickStart-style adaptation patterns.
3. Read references/troubleshooting.md before debugging import errors, backend gaps, tensor shape mismatches, unsupported model methods, target sync bugs, or convergence expectations.
4. Read references/tipc-reference.md only to classify PARL TIPC configurations; do not run the original TIPC shell launchers unless a human explicitly accepts the side effects.
5. Read references/challenge-examples.md when the task involves PARL competition examples, curriculum learning, OpenSim/L2RPN, or large distributed challenge runs.

Safe inspection helper

Run the bundled helper from this sub-skill directory or pass a source root explicitly:

python scripts/inspect_algorithm_catalog.py --backend torch
python scripts/inspect_algorithm_catalog.py --backend paddle --no-import --source-root <repo-or-package-root>
python scripts/inspect_algorithm_catalog.py --backend torch --algorithm SAC --json

The helper is read-only. It imports PARL when available, otherwise falls back to static source inspection and reports missing backends without installing packages or launching training.

Operating rules

• Set PARL_BACKEND=torch, PARL_BACKEND=paddle, or PARL_BACKEND=fluid before importing parl when the target backend matters.
• Treat Torch algorithm signatures as runtime-verified for the major built-in classes covered by the production inspection. Paddle and Fluid entries are source-backed in this skill unless the current task separately verifies those runtimes.
• Prefer tiny smoke checks and synthetic shape checks over long RL training. Do not claim benchmark reproduction from a one-episode or help-only run.
• Do not copy PARL example launchers into a user workspace unless you have replaced network downloads, long training defaults, system package changes, xparl process control, and environment-specific paths with explicit, user-approved steps.