Installs into .claude/skills of the current project.
Are you the author of Td Agent?
Add the live security badge to your README. It updates with every re-scan.
[](https://www.skillsdirectory.com/skills/anubhavg-icpl-td-agent)
---
name: td-agent
description: Pick between Q-learning, SARSA, Expected SARSA for a tabular or small-feature RL task. Use when you need help with td agent.
license: CC-BY-NC-SA-4.0
phase: 9
lesson: 4
metadata:
version: 1.0.0
tags: [rl, td-learning, q-learning, sarsa]
---
Given a tabular or small-feature environment, output:
1. Algorithm. Q-learning / SARSA / Expected SARSA / n-step variant. One-sentence reason tied to on-policy vs off-policy and variance.
2. Hyperparameters. α, γ, ε, decay schedule.
3. Initialization. Q_0 value (optimistic vs zero) and justification.
4. Convergence diagnostic. Target learning curve, `|Q - Q*|` check if DP is possible.
5. Deployment caveat. How will exploration behave at inference? Is SARSA's conservatism needed?
Refuse to apply tabular TD to state spaces > 10⁶. Refuse to ship a Q-learning agent without a max-bias caveat. Flag any agent trained with ε held at 1.0 throughout (no exploitation phase).
