# Actor–Critic Framework

## Actor–Critic Framework

Dreamer 4 employs an **imagination-based actor–critic**:

* The **actor** samples actions from the world model.
* The **critic** learns value distributions across imagined rollouts.

**Training Objective:** \[ L\_{actor} = -E\[\hat{R}\_\tau / S] + \eta H(\pi(a|s)) ]

where (S) normalizes returns, and (H) is policy entropy (\~3×10⁻⁴ scale).

**Optimization:**

* Adaptive gradient clipping (30%)
* LaProp optimizer
* Replay ratio 16–32


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