---

name: evolvable-modules description: "Use AgileRL evolvable modules, networks, architecture configs, custom network wrappers, and mutation-compatible model building blocks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

AgileRL Evolvable Modules

Use this sub-skill when a task asks for AgileRL EvolvableModule, EvolvableNetwork, net_config, MLP/CNN/LSTM/MultiInput/SimBa configuration, Q/value/actor networks, custom architectures, or wrapping ordinary PyTorch modules for AgileRL mutation compatibility.

Read First

• references/modules-and-networks.md for conceptual relationships and build patterns.
• references/api-reference.md for key classes and config objects.
• references/configuration.md for encoder_config, head_config, image/recurrent/multi-input settings.
• references/troubleshooting.md for shape, mutation, and custom module errors.
• scripts/inspect_evolvable_builders.py --help for safe tiny builder checks.

Boundaries

• Use ../training-workflows/SKILL.md for full Gymnasium training loops.
• Use ../hpo-and-mutation/SKILL.md for mutation probabilities and tournament selection.
• Use ../multi-agent-and-wrappers/SKILL.md for grouped multi-agent net_config and PettingZoo agent IDs.
• This sub-skill owns architecture selection, config validation, custom wrappers, and network construction.

Common Routes

Task	Guidance
Vector observation to discrete action values	Use QNetwork with MLP encoder/head config.
Image observation policy/value network	Use CNN encoder config and verify channels/order.
Dict/Tuple observations	Use MultiInput config with MLP/CNN/LSTM sub-configs as needed.
Recurrent/partially observable workflow	Use LSTM config and read training recurrent notes.
Custom PyTorch module	Inherit EvolvableModule when architecture should mutate, or use DummyEvolvable when only RL hyperparameters/weights should mutate.
SimBa/ResNet style architecture	Use the corresponding module/network configs and validate dimensions before training.

Safe Validation

python scripts/inspect_evolvable_builders.py --mode mlp
python scripts/inspect_evolvable_builders.py --mode dict

The helper constructs tiny Gymnasium spaces and config objects. It does not train.