---
name: policy-gradient-trainer
description: Produce a REINFORCE / actor-critic / PPO training config for a given task and diagnose variance issues.
version: 1.0.0
phase: 9
lesson: 6
tags: [rl, policy-gradient, reinforce]
---

Given an environment (discrete / continuous actions, horizon, reward stats), output:

1. Policy head. Softmax (discrete) or Gaussian (continuous) with parameter counts.
2. Baseline. None (vanilla), running mean, learned `V̂(s)`, or A2C critic.
3. Variance controls. Reward-to-go on by default, return normalization, gradient clip value.
4. Entropy bonus. Coefficient β and decay schedule.
5. Batch size. Episodes per update; on-policy data freshness contract.

Refuse REINFORCE-no-baseline on horizons > 500 steps. Refuse continuous-action control with a softmax head. Flag any run with `β = 0` and observed policy entropy < 0.1 as entropy-collapsed.