rlens v0.1.0 — first release

First public release (alpha).

rlens is a local, zero-setup workbench for understanding, debugging, and comparing
reinforcement-learning runs on a single machine — PPO, DQN, and SAC on one shared trainer,
streamed live to a built-in observability dashboard. Full tour in the
README.

Install

pip install rlens
optional extras: rlens[box2d] (LunarLander) · rlens[atari] (image observations)

Python 3.11+; CPU or Apple Silicon (MPS), no CUDA required.

In this release

• PPO, DQN, SAC on a shared trainer and telemetry layer.
• Built-in dashboard: multi-run overlay, all-metrics grid, run-comparison table, config-diff,
  gradient norms, action histograms, and inline rollout video.
• Image observations & Atari (Nature-CNN encoder, frame-stacking, uint8 replay).
• Reproducible & resumable: per-run config/version/git snapshots, full-state checkpoints,
  best-policy saving, crash-safe --resume.
• Reproduced reference returns on CartPole, Acrobot, Pendulum, and LunarLander — both PPO and
  DQN solve LunarLander-v3. See benchmarks/.

Caveats

• Single-machine scope — not distributed / multi-GPU / hosted tracking.
• The Atari pipeline is verified end-to-end, but reproducing published Atari scores needs a GPU.
• Alpha: the API may change before 1.0.

MIT licensed.