---

name: hpo-and-mutation description: "Use AgileRL evolutionary HPO, tournament selection, mutation probabilities, mutable hyperparameters, and population evolution safely." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

AgileRL HPO And Mutation

Use this sub-skill when the task is about AgileRL evolutionary hyperparameter optimization (HPO), TournamentSelection, Mutations, HyperparameterConfig, RLParameter, mutation probabilities, architecture mutation behavior, or diagnosing why a population did or did not mutate.

Read First

• references/hpo-workflows.md for the standard tournament + mutation loop.
• references/api-reference.md for key HPO classes and parameters.
• references/troubleshooting.md for invalid mutable attributes, probability, and architecture mutation failures.
• scripts/inspect_hpo_setup.py --help for a safe config-only HPO probe.

Boundaries

• Training-loop placement belongs in ../training-workflows/SKILL.md, ../multi-agent-and-wrappers/SKILL.md, ../offline-bandits-data/SKILL.md, or ../llm-fine-tuning/SKILL.md depending on the workflow.
• Architecture object construction belongs in ../evolvable-modules/SKILL.md.
• This sub-skill owns which hyperparameters are mutable, how mutation probabilities are selected, how tournament selection preserves elites, and why mutation may be a no-op.

Standard HPO Flow

1. Create a population of AgileRL agents.
2. Evaluate population fitness.
3. Select elites and the next generation with TournamentSelection.
4. Mutate offspring with Mutations.
5. Continue training with the evolved population.

from agilerl.hpo.tournament import TournamentSelection
from agilerl.hpo.mutation import Mutations
from agilerl.algorithms.core.registry import HyperparameterConfig, RLParameter

hp_config = HyperparameterConfig(
    lr=RLParameter(min=1e-4, max=1e-2),
    batch_size=RLParameter(min=32, max=256),
    learn_step=RLParameter(min=1, max=10, grow_factor=1.5, shrink_factor=0.75),
)

tournament = TournamentSelection(tournament_size=2, elitism=True, population_size=6, eval_loop=1)
mutations = Mutations(
    no_mutation=0.4,
    architecture=0.2,
    new_layer_prob=0.2,
    parameters=0.2,
    activation=0.0,
    rl_hp=0.2,
    mutation_sd=0.1,
    rand_seed=1,
    device="cpu",
)

Decision Points

• Set activation=0 when activation mutations are unsupported or not desired.
• Use HyperparameterConfig fields that exactly match algorithm attributes.
• Keep population_size, POP_SIZE, and tournament population size aligned.
• Use deterministic seeds when comparing HPO behavior.
• For LLM algorithms, do not assume architecture mutations are supported.