NoisyLinear layer on top of ensemble Q values provides state-dependent
exploration noise via h_t → learned perturbation. Replaces the fixed
P_MIN=0.015 probability floor with learned exploration (P_MIN→0.0).
- NoisyLinear constructed with in=128, out=11, σ_init=0.5
- resample_noise() each step for fresh factored noise
- forward(h_t) → noise_d added to ensemble_q_d via aux_vec_add
- 4 Adam optimizers for mu_w, sigma_w, mu_b, sigma_b
- ISV seeds for IQN ensemble α=0.5, n_tau=32, σ_init=0.5
- Target network path stays noise-free (Fortunato et al. 2017)
Local smoke: 100 steps, l_q=2.30, no crash. All three phases complete.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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