Policy Inspector

Policy Inspector

rlens now shows what your network is doing, not just its loss curve.

The new policy inspector renders per-layer weight and gradient distributions over
training as a heatmap (x = step, y = value, colour = density) — watch weights spread or
saturate and gradients collapse, layer by layer. It's the introspection W&B/TensorBoard
make you wire up by hand, available with zero setup in the local dashboard.

What's new

• Policy inspector in the dashboard — per-layer weight/gradient distribution heatmaps,
  with a layer selector, placed under the reward chart.
• Distribution capture is Trainer-owned and uniform across PPO/DQN/SAC — no per-algorithm
  code. Weights and gradients are sampled (capped per tensor) and logged as histograms over
  training; gradient norms (grad_norm/* scalars) are still there too.
• --inspect-interval flag / inspect_interval_steps config knob to control snapshot
  cadence (0 = auto ≈ 50 snapshots/run, <0 = off).
• New telemetry endpoint /api/runs/{id}/histogram_series serving the full (downsampled)
  histogram-over-time series.

Compatibility

• No breaking changes. The inspector is on by default at a coarse cadence; pass
  --inspect-interval -1 to disable.

Install / upgrade: pip install -U rlens

Full changelog: v0.1.0...v0.2.0