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name: reinforcement-learning description: "Operate ML-From-Scratch DeepQNetwork CartPole workflows, model builders, replay memory, epsilon decay, and Gym compatibility." disable-model-invocation: true metadata: disco-role: operating license: MIT

reinforcement-learning

Use this sub-skill when the task is specifically about ML-From-Scratch's DeepQNetwork workflow: CartPole setup, Gym compatibility, model-builder callbacks, replay memory, epsilon-greedy behavior, train, or play/render behavior.

Route first

• For the DQN lifecycle, use references/workflows.md.
• For Gym/NumPy compatibility, reset/step API drift, render/display failures, and state/action shape errors, use references/troubleshooting.md.
• For a bounded non-rendering smoke check, run scripts/run_dqn_smoke.py.
• For layer, optimizer, loss, and NeuralNetwork internals, route to ../deep-learning/SKILL.md.
• For package-wide install/dependency context, see ../../references/package-overview.md and ../../references/troubleshooting.md.

Operating scope

Supported package surface:

• DeepQNetwork(env_name='CartPole-v1', epsilon=1, gamma=0.9, decay_rate=0.005, min_epsilon=0.1)
• dqn.set_model(model_builder) where the callback accepts n_inputs and n_outputs
• dqn.train(n_epochs=500, batch_size=32) for educational training loops
• dqn.play(n_epochs) only when rendering/display support is intentionally available

Do not present this repository as a full RL framework. It is an educational DQN implementation with a small replay buffer, no target network, no vectorized environments, and version-sensitive Gym assumptions.

Default stance

Start with a one-epoch, no-render smoke before longer training. Prefer gym==0.25.x with NumPy <2 for compatibility. If the user is on newer Gym APIs, normalize reset to an observation and step to (obs, reward, done, info) before calling DeepQNetwork.train.