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name: optimization-meta-rl description: "Routes Papers-in-100-Lines optimizer, activation, layer, meta-learning, hypergradient, Deep Image Prior, and reinforcement-learning tasks." disable-model-invocation: true metadata: disco-role: operating license: MIT

Optimization, Meta-Learning, and RL

Use this sub-skill when the user asks about the compact optimizer, activation, layer, meta-learning, hyperparameter optimization, Deep Image Prior, or reinforcement-learning entries in Papers-in-100-Lines.

Read these bundled files

• Workflow guide maps the family entries, reusable classes/functions, adaptation patterns, long-loop controls, and validation signals.
• Troubleshooting covers Keras/MNIST downloads, hard-coded CUDA, optimizer state pitfalls, autograd updates, gym/ALE/ROM setup, and plot/output side effects.
• Implementation index lists the owner group and entry metadata.
• Dependency and backend guide explains why older torch/Keras/gym pins should be isolated per entry.
• estimate_training_steps.py estimates update counts before running long training examples.

Trigger routes

• Optimizers: Adam and RAdam implementations, optimizer state, bias correction, variance rectification, or comparing to torch.optim.
• Layers and activations: Maxout, Network-in-Network, ELU, GELU, SELU/SNN.
• Meta-learning and hypergradients: MAML, Reptile, learned initializations, implicit differentiation, weight-decay hyperparameters.
• Deep Image Prior: image reconstruction via untrained convolutional model and noise input.
• Reinforcement learning: DQN, Double DQN, PPO, Atari wrappers, replay buffers, long environment loops.

Safe workflow

1. Query the catalog if the target is ambiguous:

   python ../../scripts/query_implementation_index.py --group optimization-meta-rl --query "adam"
   

2. Read Workflow guide for the family and pick the smallest reusable component: optimizer class, activation module, meta-update loop, hypergradient helper, or RL network.

3. Estimate loop size before running:

   python scripts/estimate_training_steps.py --nb-epochs 70000 --batch-size 10 --eval-interval 1000
   

4. For a quick check, create a CPU toy model/environment and run one or a few updates. Do not launch million-step Atari or 70k-step MAML loops as smoke tests.

5. Preserve autograd semantics when adapting meta-learning or hypergradient code; copying .data updates or cloned parameters blindly can change the algorithm.

Boundaries

Route GANs, VAEs, flows, diffusion, DreamBooth, Stable Diffusion, and image translation to generative-models. Route NeRF, 3D Gaussian splatting, SIREN/MFN, camera, ray, and 3D reconstruction tasks to neural-rendering-3d. Use paper-catalog-and-execution for catalog lookup and first-run planning.