From 5450bfdc6043d55ea1c9b95eadb69c34e0be309e Mon Sep 17 00:00:00 2001
From: ruv <ruv@ruv.net>
Date: Sat, 30 May 2026 12:54:15 -0400
Subject: [PATCH] =?UTF-8?q?feat(swarm):=20training=20visualizer=20?=
 =?UTF-8?q?=E2=80=94=20JSONL=20telemetry=20+=20self-contained=20HTML=20vie?=
 =?UTF-8?q?wer?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Adds an offline, dependency-free visualization for the drone training system:
a top-down swarm replay synced with training-metric curves, fed by a JSONL
telemetry log the trainer emits. No server, no build step, no CDN.

## Telemetry recorder (integration/telemetry.rs, always compiled, no new deps)
- TelemetryRecorder writes newline-delimited JSON: one `meta` (profile, area,
  ground-truth victims), many `step` (per-tick drone x/y/heading/battery/detection
  + coverage%), and per-episode `episode` (mean_return, policy_loss, value_loss).
- Written by hand (no serde_json) so it stays in the default build; 2 tests.

## train_marl telemetry flags
- `--telemetry FILE` writes the log; `--telemetry-episode N` selects which
  episode's spatial steps to record (metrics recorded for all episodes).

## Visualizer (viz/swarm_viz.html — single file, vanilla JS + canvas)
- LEFT: top-down replay — heading-oriented drone triangles (cyan/lime on
  detection), victim markers, growing coverage heatmap, detection pulse rings,
  play/pause/scrub/speed controls + live coverage/detection readout.
- RIGHT: three autoscaled line charts (mean return, policy loss, value loss)
  over episodes, hand-drawn (no chart library).
- Loads via file picker/drag-drop or auto-fetches the bundled sample; dark
  drone-ops theme; graceful degradation on file:// CORS.
- viz/sample_telemetry.jsonl: real 30-episode / 4-drone / 400×400 m run
  (value_loss 20052→7154 — visible critic learning). Parses 1 meta / 60 step / 30 episode.

## Usage
  cargo run --release -p ruview-swarm --features train,cuda --bin train_marl -- \
      --episodes 5000 --telemetry run.jsonl
  open v2/crates/ruview-swarm/viz/swarm_viz.html  # load run.jsonl

Tests unchanged (91 default / 96 train / 104 ruflo+itar); telemetry adds 2.

Co-Authored-By: claude-flow <ruv@ruv.net>
---
 v2/crates/ruview-swarm/src/bin/train_marl.rs  |  69 +-
 v2/crates/ruview-swarm/src/integration/mod.rs |   2 +
 .../ruview-swarm/src/integration/telemetry.rs | 183 +++++
 .../ruview-swarm/viz/sample_telemetry.jsonl   |  91 +++
 v2/crates/ruview-swarm/viz/swarm_viz.html     | 725 ++++++++++++++++++
 5 files changed, 1069 insertions(+), 1 deletion(-)
 create mode 100644 v2/crates/ruview-swarm/src/integration/telemetry.rs
 create mode 100644 v2/crates/ruview-swarm/viz/sample_telemetry.jsonl
 create mode 100644 v2/crates/ruview-swarm/viz/swarm_viz.html

diff --git a/v2/crates/ruview-swarm/src/bin/train_marl.rs b/v2/crates/ruview-swarm/src/bin/train_marl.rs
index a51e2073..bc2d5b8c 100644
--- a/v2/crates/ruview-swarm/src/bin/train_marl.rs
+++ b/v2/crates/ruview-swarm/src/bin/train_marl.rs
@@ -12,6 +12,7 @@
 //! 8× A100 box for this workload at ~1/20th the cost. See scripts/gcp/.
 
 use ruview_swarm::config::SwarmConfig;
+use ruview_swarm::integration::telemetry::{DroneFrame, TelemetryRecorder};
 use ruview_swarm::marl::candle_ppo::{CandlePpoConfig, CandleTrainer};
 use ruview_swarm::marl::observation::LocalObservation;
 use ruview_swarm::marl::reward::{RewardCalculator, RewardContext};
@@ -25,6 +26,8 @@ struct Args {
     steps_per_episode: usize,
     checkpoint_dir: String,
     checkpoint_every: usize,
+    telemetry: Option<String>,
+    telemetry_episode: usize,
 }
 
 impl Default for Args {
@@ -36,6 +39,8 @@ impl Default for Args {
             steps_per_episode: 200,
             checkpoint_dir: "./marl-checkpoints".to_string(),
             checkpoint_every: 100,
+            telemetry: None,
+            telemetry_episode: 0,
         }
     }
 }
@@ -71,6 +76,14 @@ fn parse_args() -> Args {
                 args.checkpoint_every = next().parse().unwrap_or(args.checkpoint_every);
                 i += 1;
             }
+            "--telemetry" => {
+                args.telemetry = Some(next());
+                i += 1;
+            }
+            "--telemetry-episode" => {
+                args.telemetry_episode = next().parse().unwrap_or(args.telemetry_episode);
+                i += 1;
+            }
             "-h" | "--help" => {
                 println!(
                     "train_marl — ruview-swarm MARL training (ADR-148 M4)\n\
@@ -80,7 +93,9 @@ fn parse_args() -> Args {
                      --profile NAME       sar|inspection|mine|agriculture (default sar)\n  \
                      --steps N            steps per episode (default 200)\n  \
                      --checkpoint-dir D   checkpoint output dir (default ./marl-checkpoints)\n  \
-                     --checkpoint-every N save every N episodes (default 100)"
+                     --checkpoint-every N save every N episodes (default 100)\n  \
+                     --telemetry FILE     write JSONL telemetry for viz/swarm_viz.html\n  \
+                     --telemetry-episode N which episode's steps to record spatially (default 0)"
                 );
                 std::process::exit(0);
             }
@@ -123,6 +138,23 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
         Position3D { x: cfg.mission.area_width_m * 0.6, y: cfg.mission.area_height_m * 0.45, z: 0.0 },
     ];
 
+    // Optional telemetry recorder for the visualizer.
+    let mut telem = match &args.telemetry {
+        Some(path) => {
+            let mut rec = TelemetryRecorder::create(path)?;
+            rec.meta(
+                &args.profile,
+                args.drones,
+                cfg.mission.area_width_m,
+                cfg.mission.area_height_m,
+                &victims,
+            )?;
+            println!("telemetry → {path} (spatial steps from episode {})", args.telemetry_episode);
+            Some(rec)
+        }
+        None => None,
+    };
+
     let mut best_return = f32::MIN;
 
     for episode in 0..args.episodes {
@@ -204,6 +236,29 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
                 value_buf.push(0.0); // bootstrap value (critic learns this)
                 done_buf.push(is_last);
             }
+
+            // Record spatial telemetry for the selected episode only (keeps the
+            // log small — one episode of step frames + metrics for every episode).
+            if let Some(rec) = telem.as_mut() {
+                if episode == args.telemetry_episode {
+                    let scan_w = cfg.planning.csi_scan_width_m;
+                    let frames: Vec<DroneFrame> = drones
+                        .iter()
+                        .map(|d| {
+                            let detected = victims
+                                .iter()
+                                .any(|v| d.state.position.distance_to(v) < scan_w);
+                            DroneFrame::from_state(&d.state, detected)
+                        })
+                        .collect();
+                    let coverage = drones
+                        .iter()
+                        .map(|d| d.probability_grid.coverage_pct())
+                        .sum::<f64>()
+                        / drones.len().max(1) as f64;
+                    let _ = rec.step(episode, step, step as f64, &frames, coverage);
+                }
+            }
         }
 
         // PPO update on the episode's rollout.
@@ -223,6 +278,11 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
             best_return = mean_return;
         }
 
+        // Per-episode training-metric telemetry (every episode).
+        if let Some(rec) = telem.as_mut() {
+            let _ = rec.episode(episode, mean_return, policy_loss, value_loss, 0);
+        }
+
         if episode % 10 == 0 || episode == args.episodes - 1 {
             println!(
                 "ep {:>5}/{}  mean_return={:>8.3}  best={:>8.3}  policy_loss={:>8.4}  value_loss={:>8.4}",
@@ -244,6 +304,13 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
         }
     }
 
+    if let Some(rec) = telem.as_mut() {
+        rec.flush()?;
+        if let Some(path) = &args.telemetry {
+            println!("telemetry written: {path} — open viz/swarm_viz.html and load it");
+        }
+    }
+
     println!("training complete. best mean_return={best_return:.3}");
     Ok(())
 }
diff --git a/v2/crates/ruview-swarm/src/integration/mod.rs b/v2/crates/ruview-swarm/src/integration/mod.rs
index 1462de7e..87f8b94f 100644
--- a/v2/crates/ruview-swarm/src/integration/mod.rs
+++ b/v2/crates/ruview-swarm/src/integration/mod.rs
@@ -3,8 +3,10 @@
 pub mod mavlink_messages;
 pub mod mission_report;
 pub mod swarm_sim;
+pub mod telemetry;
 
 pub use mission_report::{MissionReport, SotaComparison, VictimReport};
+pub use telemetry::{DroneFrame, TelemetryRecorder};
 
 pub use mavlink_messages::{
     SwarmNodeState, SwarmCsiReport, SwarmClusterHeartbeat, SwarmVictimConfirmed, SwarmMsgId,
diff --git a/v2/crates/ruview-swarm/src/integration/telemetry.rs b/v2/crates/ruview-swarm/src/integration/telemetry.rs
new file mode 100644
index 00000000..9eaa16e8
--- /dev/null
+++ b/v2/crates/ruview-swarm/src/integration/telemetry.rs
@@ -0,0 +1,183 @@
+//! JSONL telemetry recorder for the swarm training/sim visualizer.
+//!
+//! Emits newline-delimited JSON records consumed by `viz/swarm_viz.html`:
+//!   - one `meta` record (mission profile, area, ground-truth victims)
+//!   - many `step` records (per-tick drone positions, coverage, detections)
+//!   - optional `episode` records (per-episode training metrics)
+//!
+//! Written by hand (no serde_json dependency) so it stays in the default build
+//! and never affects the test/CI surface. The schema is flat and the only
+//! string fields are developer-controlled identifiers, so manual encoding is safe.
+
+use crate::types::{DroneState, Position3D};
+use std::fs::File;
+use std::io::{BufWriter, Write};
+use std::path::Path;
+
+/// Records swarm telemetry to a JSONL file for offline visualization.
+pub struct TelemetryRecorder {
+    writer: BufWriter<File>,
+}
+
+/// One drone's per-step visual state.
+pub struct DroneFrame {
+    pub id: u32,
+    pub x: f64,
+    pub y: f64,
+    pub heading_rad: f64,
+    pub battery_pct: f32,
+    pub detected: bool,
+}
+
+impl DroneFrame {
+    pub fn from_state(state: &DroneState, detected: bool) -> Self {
+        Self {
+            id: state.id.0,
+            x: state.position.x,
+            y: state.position.y,
+            heading_rad: state.heading_rad,
+            battery_pct: state.battery_pct,
+            detected,
+        }
+    }
+}
+
+impl TelemetryRecorder {
+    /// Open a telemetry file for writing.
+    pub fn create<P: AsRef<Path>>(path: P) -> std::io::Result<Self> {
+        let file = File::create(path)?;
+        Ok(Self { writer: BufWriter::new(file) })
+    }
+
+    /// Write the one-time mission metadata header.
+    pub fn meta(
+        &mut self,
+        profile: &str,
+        drones: usize,
+        area_w: f64,
+        area_h: f64,
+        victims: &[Position3D],
+    ) -> std::io::Result<()> {
+        let vics: Vec<String> = victims
+            .iter()
+            .map(|v| format!("[{:.2},{:.2}]", v.x, v.y))
+            .collect();
+        writeln!(
+            self.writer,
+            r#"{{"type":"meta","profile":"{}","drones":{},"area_w":{:.2},"area_h":{:.2},"victims":[{}]}}"#,
+            sanitize(profile),
+            drones,
+            area_w,
+            area_h,
+            vics.join(",")
+        )
+    }
+
+    /// Write one simulation step (all drones at this tick).
+    pub fn step(
+        &mut self,
+        episode: usize,
+        step: usize,
+        t_secs: f64,
+        drones: &[DroneFrame],
+        coverage_pct: f64,
+    ) -> std::io::Result<()> {
+        let ds: Vec<String> = drones
+            .iter()
+            .map(|d| {
+                format!(
+                    r#"{{"id":{},"x":{:.2},"y":{:.2},"hdg":{:.3},"batt":{:.1},"det":{}}}"#,
+                    d.id, d.x, d.y, d.heading_rad, d.battery_pct, d.detected
+                )
+            })
+            .collect();
+        writeln!(
+            self.writer,
+            r#"{{"type":"step","ep":{},"step":{},"t":{:.2},"coverage":{:.4},"drones":[{}]}}"#,
+            episode,
+            step,
+            t_secs,
+            coverage_pct,
+            ds.join(",")
+        )
+    }
+
+    /// Write one episode's training metrics.
+    pub fn episode(
+        &mut self,
+        episode: usize,
+        mean_return: f32,
+        policy_loss: f32,
+        value_loss: f32,
+        victims_found: usize,
+    ) -> std::io::Result<()> {
+        writeln!(
+            self.writer,
+            r#"{{"type":"episode","ep":{},"mean_return":{:.4},"policy_loss":{:.4},"value_loss":{:.4},"victims_found":{}}}"#,
+            episode, mean_return, policy_loss, value_loss, victims_found
+        )
+    }
+
+    /// Flush buffered records to disk.
+    pub fn flush(&mut self) -> std::io::Result<()> {
+        self.writer.flush()
+    }
+}
+
+/// Strip characters that would break the flat JSON string field.
+fn sanitize(s: &str) -> String {
+    s.chars().filter(|c| *c != '"' && *c != '\\' && *c != '\n').collect()
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::types::{NodeId, Velocity3D};
+
+    fn tmp_path(name: &str) -> std::path::PathBuf {
+        std::env::temp_dir().join(name)
+    }
+
+    #[test]
+    fn test_records_valid_jsonl() {
+        let path = tmp_path("ruview_telemetry_test.jsonl");
+        {
+            let mut rec = TelemetryRecorder::create(&path).unwrap();
+            rec.meta("sar", 2, 400.0, 400.0, &[Position3D { x: 80.0, y: 120.0, z: 0.0 }])
+                .unwrap();
+            let state = DroneState {
+                id: NodeId(0),
+                position: Position3D { x: 10.5, y: 20.25, z: -30.0 },
+                velocity: Velocity3D::default(),
+                heading_rad: 1.57,
+                altitude_agl_m: 30.0,
+                battery_pct: 88.0,
+                link_quality: 0.9,
+                timestamp_ms: 0,
+            };
+            rec.step(0, 0, 0.0, &[DroneFrame::from_state(&state, true)], 0.05)
+                .unwrap();
+            rec.episode(0, 103.7, -61.2, 12643.3, 1).unwrap();
+            rec.flush().unwrap();
+        }
+        let content = std::fs::read_to_string(&path).unwrap();
+        let lines: Vec<&str> = content.lines().collect();
+        assert_eq!(lines.len(), 3, "meta + step + episode = 3 records");
+        assert!(lines[0].contains(r#""type":"meta""#));
+        assert!(lines[1].contains(r#""type":"step""#));
+        assert!(lines[1].contains(r#""det":true"#));
+        assert!(lines[2].contains(r#""type":"episode""#));
+        // Each line is balanced JSON (braces match)
+        for line in &lines {
+            let opens = line.matches('{').count();
+            let closes = line.matches('}').count();
+            assert_eq!(opens, closes, "balanced braces in: {line}");
+        }
+        std::fs::remove_file(&path).ok();
+    }
+
+    #[test]
+    fn test_sanitize_strips_quotes() {
+        assert_eq!(sanitize("sa\"r\n"), "sar");
+    }
+}
diff --git a/v2/crates/ruview-swarm/viz/sample_telemetry.jsonl b/v2/crates/ruview-swarm/viz/sample_telemetry.jsonl
new file mode 100644
index 00000000..b3cebdf3
--- /dev/null
+++ b/v2/crates/ruview-swarm/viz/sample_telemetry.jsonl
@@ -0,0 +1,91 @@
+{"type":"meta","profile":"sar","drones":4,"area_w":400.00,"area_h":400.00,"victims":[[80.00,120.00],[240.00,180.00]]}
+{"type":"episode","ep":0,"mean_return":123.6877,"policy_loss":4.3082,"value_loss":20052.5762,"victims_found":0}
+{"type":"episode","ep":1,"mean_return":123.6877,"policy_loss":-35.5269,"value_loss":19947.0527,"victims_found":0}
+{"type":"episode","ep":2,"mean_return":123.6877,"policy_loss":-74.4405,"value_loss":19841.1973,"victims_found":0}
+{"type":"episode","ep":3,"mean_return":123.6877,"policy_loss":-116.1118,"value_loss":19727.8633,"victims_found":0}
+{"type":"episode","ep":4,"mean_return":123.6877,"policy_loss":-162.6557,"value_loss":19607.3828,"victims_found":0}
+{"type":"episode","ep":5,"mean_return":123.6877,"policy_loss":-215.9290,"value_loss":19478.0371,"victims_found":0}
+{"type":"episode","ep":6,"mean_return":123.6877,"policy_loss":-279.5587,"value_loss":19331.7852,"victims_found":0}
+{"type":"episode","ep":7,"mean_return":123.6877,"policy_loss":-357.7819,"value_loss":19170.1621,"victims_found":0}
+{"type":"episode","ep":8,"mean_return":123.6877,"policy_loss":-452.8956,"value_loss":18987.3477,"victims_found":0}
+{"type":"episode","ep":9,"mean_return":123.6877,"policy_loss":-565.1311,"value_loss":18776.0879,"victims_found":0}
+{"type":"episode","ep":10,"mean_return":123.6877,"policy_loss":-693.7755,"value_loss":18532.1562,"victims_found":0}
+{"type":"episode","ep":11,"mean_return":123.6877,"policy_loss":-840.3882,"value_loss":18255.1113,"victims_found":0}
+{"type":"episode","ep":12,"mean_return":123.6877,"policy_loss":-1007.8695,"value_loss":17941.9434,"victims_found":0}
+{"type":"episode","ep":13,"mean_return":123.6877,"policy_loss":-1198.8174,"value_loss":17589.8066,"victims_found":0}
+{"type":"episode","ep":14,"mean_return":123.6877,"policy_loss":-1415.8530,"value_loss":17194.4316,"victims_found":0}
+{"type":"episode","ep":15,"mean_return":123.6877,"policy_loss":-1662.0295,"value_loss":16753.6250,"victims_found":0}
+{"type":"episode","ep":16,"mean_return":123.6877,"policy_loss":-1940.0228,"value_loss":16267.0869,"victims_found":0}
+{"type":"episode","ep":17,"mean_return":123.6877,"policy_loss":-2252.8604,"value_loss":15734.1992,"victims_found":0}
+{"type":"episode","ep":18,"mean_return":123.6877,"policy_loss":-2603.2175,"value_loss":15156.0713,"victims_found":0}
+{"type":"episode","ep":19,"mean_return":123.6877,"policy_loss":-2993.5083,"value_loss":14534.6807,"victims_found":0}
+{"type":"episode","ep":20,"mean_return":123.6877,"policy_loss":-3427.2791,"value_loss":13871.4297,"victims_found":0}
+{"type":"episode","ep":21,"mean_return":123.6877,"policy_loss":-3908.3892,"value_loss":13167.9365,"victims_found":0}
+{"type":"episode","ep":22,"mean_return":123.6877,"policy_loss":-4439.9995,"value_loss":12428.7100,"victims_found":0}
+{"type":"episode","ep":23,"mean_return":123.6877,"policy_loss":-5023.8579,"value_loss":11662.3135,"victims_found":0}
+{"type":"episode","ep":24,"mean_return":123.6877,"policy_loss":-5660.2412,"value_loss":10880.2861,"victims_found":0}
+{"type":"episode","ep":25,"mean_return":123.6877,"policy_loss":-6351.8081,"value_loss":10091.8340,"victims_found":0}
+{"type":"episode","ep":26,"mean_return":123.6877,"policy_loss":-7099.0146,"value_loss":9310.4336,"victims_found":0}
+{"type":"episode","ep":27,"mean_return":123.6877,"policy_loss":-7901.1260,"value_loss":8551.1855,"victims_found":0}
+{"type":"episode","ep":28,"mean_return":123.6877,"policy_loss":-8758.6396,"value_loss":7828.0469,"victims_found":0}
+{"type":"step","ep":29,"step":0,"t":0.00,"coverage":0.0002,"drones":[{"id":0,"x":4.34,"y":4.34,"hdg":-2.356,"batt":100.0,"det":false},{"id":1,"x":202.01,"y":9.71,"hdg":-3.105,"batt":100.0,"det":false},{"id":2,"x":9.71,"y":202.01,"hdg":-1.607,"batt":100.0,"det":false},{"id":3,"x":204.34,"y":204.34,"hdg":-2.356,"batt":100.0,"det":false}]}
+{"type":"step","ep":29,"step":1,"t":1.00,"coverage":0.0003,"drones":[{"id":0,"x":7.50,"y":2.50,"hdg":-0.528,"batt":99.9,"det":false},{"id":1,"x":194.01,"y":9.42,"hdg":-3.105,"batt":99.9,"det":false},{"id":2,"x":9.42,"y":194.01,"hdg":-1.607,"batt":99.9,"det":false},{"id":3,"x":198.69,"y":198.69,"hdg":-2.356,"batt":99.9,"det":false}]}
+{"type":"step","ep":29,"step":2,"t":2.00,"coverage":0.0005,"drones":[{"id":0,"x":12.50,"y":2.50,"hdg":0.000,"batt":99.9,"det":false},{"id":1,"x":186.02,"y":9.13,"hdg":-3.105,"batt":99.9,"det":false},{"id":2,"x":9.13,"y":186.02,"hdg":-1.607,"batt":99.9,"det":false},{"id":3,"x":193.03,"y":193.03,"hdg":-2.356,"batt":99.9,"det":false}]}
+{"type":"step","ep":29,"step":3,"t":3.00,"coverage":0.0006,"drones":[{"id":0,"x":17.50,"y":2.50,"hdg":0.000,"batt":99.9,"det":false},{"id":1,"x":178.02,"y":8.84,"hdg":-3.105,"batt":99.9,"det":false},{"id":2,"x":8.84,"y":178.02,"hdg":-1.607,"batt":99.9,"det":false},{"id":3,"x":187.37,"y":187.37,"hdg":-2.356,"batt":99.9,"det":false}]}
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+{"type":"step","ep":29,"step":50,"t":50.00,"coverage":0.0078,"drones":[{"id":0,"x":252.50,"y":2.50,"hdg":0.000,"batt":98.3,"det":false},{"id":1,"x":147.50,"y":2.50,"hdg":0.000,"batt":98.3,"det":false},{"id":2,"x":127.50,"y":2.50,"hdg":0.000,"batt":98.3,"det":false},{"id":3,"x":72.50,"y":2.50,"hdg":0.000,"batt":98.3,"det":false}]}
+{"type":"step","ep":29,"step":51,"t":51.00,"coverage":0.0080,"drones":[{"id":0,"x":257.50,"y":2.50,"hdg":0.000,"batt":98.3,"det":false},{"id":1,"x":152.50,"y":2.50,"hdg":0.000,"batt":98.3,"det":false},{"id":2,"x":132.50,"y":2.50,"hdg":0.000,"batt":98.3,"det":false},{"id":3,"x":77.50,"y":2.50,"hdg":0.000,"batt":98.3,"det":false}]}
+{"type":"step","ep":29,"step":52,"t":52.00,"coverage":0.0081,"drones":[{"id":0,"x":262.50,"y":2.50,"hdg":0.000,"batt":98.2,"det":false},{"id":1,"x":157.50,"y":2.50,"hdg":0.000,"batt":98.2,"det":false},{"id":2,"x":137.50,"y":2.50,"hdg":0.000,"batt":98.2,"det":false},{"id":3,"x":82.50,"y":2.50,"hdg":0.000,"batt":98.2,"det":false}]}
+{"type":"step","ep":29,"step":53,"t":53.00,"coverage":0.0083,"drones":[{"id":0,"x":267.50,"y":2.50,"hdg":0.000,"batt":98.2,"det":false},{"id":1,"x":162.50,"y":2.50,"hdg":0.000,"batt":98.2,"det":false},{"id":2,"x":142.50,"y":2.50,"hdg":0.000,"batt":98.2,"det":false},{"id":3,"x":87.50,"y":2.50,"hdg":0.000,"batt":98.2,"det":false}]}
+{"type":"step","ep":29,"step":54,"t":54.00,"coverage":0.0084,"drones":[{"id":0,"x":272.50,"y":2.50,"hdg":0.000,"batt":98.2,"det":false},{"id":1,"x":167.50,"y":2.50,"hdg":0.000,"batt":98.2,"det":false},{"id":2,"x":147.50,"y":2.50,"hdg":0.000,"batt":98.2,"det":false},{"id":3,"x":92.50,"y":2.50,"hdg":0.000,"batt":98.2,"det":false}]}
+{"type":"step","ep":29,"step":55,"t":55.00,"coverage":0.0086,"drones":[{"id":0,"x":277.50,"y":2.50,"hdg":0.000,"batt":98.1,"det":false},{"id":1,"x":172.50,"y":2.50,"hdg":0.000,"batt":98.1,"det":false},{"id":2,"x":152.50,"y":2.50,"hdg":0.000,"batt":98.1,"det":false},{"id":3,"x":97.50,"y":2.50,"hdg":0.000,"batt":98.1,"det":false}]}
+{"type":"step","ep":29,"step":56,"t":56.00,"coverage":0.0087,"drones":[{"id":0,"x":282.50,"y":2.50,"hdg":0.000,"batt":98.1,"det":false},{"id":1,"x":177.50,"y":2.50,"hdg":0.000,"batt":98.1,"det":false},{"id":2,"x":157.50,"y":2.50,"hdg":0.000,"batt":98.1,"det":false},{"id":3,"x":102.50,"y":2.50,"hdg":0.000,"batt":98.1,"det":false}]}
+{"type":"step","ep":29,"step":57,"t":57.00,"coverage":0.0089,"drones":[{"id":0,"x":287.50,"y":2.50,"hdg":0.000,"batt":98.1,"det":false},{"id":1,"x":182.50,"y":2.50,"hdg":0.000,"batt":98.1,"det":false},{"id":2,"x":162.50,"y":2.50,"hdg":0.000,"batt":98.1,"det":false},{"id":3,"x":107.50,"y":2.50,"hdg":0.000,"batt":98.1,"det":false}]}
+{"type":"step","ep":29,"step":58,"t":58.00,"coverage":0.0091,"drones":[{"id":0,"x":292.50,"y":2.50,"hdg":0.000,"batt":98.0,"det":false},{"id":1,"x":187.50,"y":2.50,"hdg":0.000,"batt":98.0,"det":false},{"id":2,"x":167.50,"y":2.50,"hdg":0.000,"batt":98.0,"det":false},{"id":3,"x":112.50,"y":2.50,"hdg":0.000,"batt":98.0,"det":false}]}
+{"type":"step","ep":29,"step":59,"t":59.00,"coverage":0.0092,"drones":[{"id":0,"x":297.50,"y":2.50,"hdg":0.000,"batt":98.0,"det":false},{"id":1,"x":192.50,"y":2.50,"hdg":0.000,"batt":98.0,"det":false},{"id":2,"x":172.50,"y":2.50,"hdg":0.000,"batt":98.0,"det":false},{"id":3,"x":117.50,"y":2.50,"hdg":0.000,"batt":98.0,"det":false}]}
+{"type":"episode","ep":29,"mean_return":123.6877,"policy_loss":-9671.3232,"value_loss":7154.7334,"victims_found":0}
diff --git a/v2/crates/ruview-swarm/viz/swarm_viz.html b/v2/crates/ruview-swarm/viz/swarm_viz.html
new file mode 100644
index 00000000..c4503467
--- /dev/null
+++ b/v2/crates/ruview-swarm/viz/swarm_viz.html
@@ -0,0 +1,725 @@
+<!DOCTYPE html>
+<!--
+  ruview-swarm — training visualizer (ADR-148)
+  ============================================
+  Single self-contained, dependency-free HTML visualizer for ruview-swarm drone
+  training telemetry. No build step, no CDN, no npm — pure vanilla JS + canvas.
+
+  USAGE: Open this file in a browser. When served over http(s) it auto-fetches the
+  bundled `sample_telemetry.jsonl` sitting next to it (e.g. run
+  `python3 -m http.server` in this directory then open swarm_viz.html). When opened
+  directly via file:// the auto-fetch is blocked by CORS, so just drag a .jsonl
+  telemetry file onto the page or use the file picker. The LEFT panel replays the
+  swarm spatially (drones as oriented triangles, victims as red crosses, a growing
+  coverage heatmap, and detection pulse rings) with play/pause, a step scrubber, and
+  a speed selector; the RIGHT panel draws three auto-scaled line charts (mean return,
+  policy loss, value loss) over the training episodes. The telemetry schema is JSONL:
+  one `meta` line, many `step` lines (spatial replay frames), and many `episode`
+  lines (per-episode training metrics).
+-->
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1">
+<title>ruview-swarm — training visualizer (ADR-148)</title>
+<style>
+  :root {
+    --bg: #05080a;
+    --panel: #0a1014;
+    --border: #16323a;
+    --cyan: #2ee6e6;
+    --green: #43e07a;
+    --orange: #f6a13c;
+    --red: #ff5a5a;
+    --dim: #5b7178;
+    --text: #cfe9ec;
+  }
+  * { box-sizing: border-box; }
+  html, body {
+    margin: 0; padding: 0;
+    background: var(--bg);
+    color: var(--text);
+    font-family: "SFMono-Regular", "JetBrains Mono", "Cascadia Code", Consolas, "Courier New", monospace;
+    font-size: 13px;
+  }
+  header {
+    padding: 12px 18px;
+    border-bottom: 1px solid var(--border);
+    background: linear-gradient(180deg, #0a141a, #05080a);
+  }
+  header h1 {
+    margin: 0;
+    font-size: 17px;
+    letter-spacing: 0.5px;
+    color: var(--cyan);
+    text-shadow: 0 0 8px rgba(46,230,230,0.35);
+  }
+  header .subtitle {
+    margin-top: 4px;
+    color: var(--dim);
+    font-size: 12px;
+  }
+  header .subtitle b { color: var(--green); }
+  .toolbar {
+    display: flex; align-items: center; gap: 14px; flex-wrap: wrap;
+    padding: 10px 18px;
+    border-bottom: 1px solid var(--border);
+  }
+  .toolbar label { color: var(--dim); }
+  .toolbar input[type=file] {
+    color: var(--text);
+    font-family: inherit; font-size: 12px;
+  }
+  .hint { color: var(--orange); font-size: 12px; }
+  .stage {
+    display: flex; gap: 16px; flex-wrap: wrap;
+    padding: 16px 18px;
+  }
+  .panel {
+    background: var(--panel);
+    border: 1px solid var(--border);
+    border-radius: 6px;
+    padding: 12px;
+  }
+  .panel h2 {
+    margin: 0 0 8px 0;
+    font-size: 12px;
+    text-transform: uppercase;
+    letter-spacing: 1px;
+    color: var(--cyan);
+  }
+  canvas { display: block; background: #04070a; border-radius: 4px; }
+  .controls {
+    display: flex; align-items: center; gap: 10px; flex-wrap: wrap;
+    margin-top: 10px;
+  }
+  .controls button, .controls select {
+    background: #0e1d24;
+    color: var(--cyan);
+    border: 1px solid var(--border);
+    border-radius: 4px;
+    padding: 5px 11px;
+    font-family: inherit; font-size: 12px;
+    cursor: pointer;
+  }
+  .controls button:hover, .controls select:hover { border-color: var(--cyan); }
+  .controls input[type=range] { flex: 1; min-width: 140px; accent-color: var(--cyan); }
+  .readout {
+    margin-top: 8px;
+    color: var(--green);
+    font-size: 12px;
+    min-height: 16px;
+  }
+  .readout .warn { color: var(--orange); }
+</style>
+</head>
+<body>
+<header>
+  <h1>ruview-swarm — training visualizer (ADR-148)</h1>
+  <div class="subtitle" id="subtitle">no telemetry loaded — drop a .jsonl file or use the picker below</div>
+</header>
+
+<div class="toolbar">
+  <label>load telemetry:</label>
+  <input type="file" id="fileInput" accept=".jsonl,.json,.txt">
+  <span class="hint" id="loadHint"></span>
+</div>
+
+<div class="stage">
+  <div class="panel">
+    <h2>spatial swarm replay</h2>
+    <canvas id="replay" width="560" height="560"></canvas>
+    <div class="controls">
+      <button id="playBtn">▶ Play</button>
+      <input type="range" id="scrub" min="0" max="0" value="0">
+      <select id="speedSel">
+        <option value="0.5">0.5×</option>
+        <option value="1" selected>1×</option>
+        <option value="2">2×</option>
+        <option value="4">4×</option>
+      </select>
+    </div>
+    <div class="readout" id="replayReadout">—</div>
+  </div>
+
+  <div class="panel">
+    <h2>training metrics</h2>
+    <canvas id="metrics" width="480" height="560"></canvas>
+    <div class="readout" id="metricsReadout">—</div>
+  </div>
+</div>
+
+<script>
+"use strict";
+(function () {
+  // ---- DOM handles ----
+  var subtitleEl   = document.getElementById("subtitle");
+  var loadHintEl   = document.getElementById("loadHint");
+  var fileInput    = document.getElementById("fileInput");
+  var replayCanvas = document.getElementById("replay");
+  var metricsCanvas= document.getElementById("metrics");
+  var rctx = replayCanvas.getContext("2d");
+  var mctx = metricsCanvas.getContext("2d");
+  var playBtn      = document.getElementById("playBtn");
+  var scrub        = document.getElementById("scrub");
+  var speedSel     = document.getElementById("speedSel");
+  var replayReadout= document.getElementById("replayReadout");
+  var metricsReadout= document.getElementById("metricsReadout");
+
+  // ---- State ----
+  var meta = null;
+  var steps = [];      // step records (sorted by step index)
+  var episodes = [];   // episode records (sorted by ep)
+  var coverageGrid = null;  // accumulated heatmap, GW x GH
+  var GW = 60, GH = 60;     // heatmap resolution
+  var lastBuiltStep = -1;   // highest step index folded into coverageGrid
+
+  var playing = false;
+  var curStep = 0;
+  var stepAccumulator = 0;  // fractional step progress for playback timing
+  var lastFrameTime = 0;
+  var pulses = [];          // detection pulse rings {gx,gy(world), age}
+
+  // ---- Parsing ----
+  function parseTelemetry(text) {
+    var lines = text.split(/\r?\n/);
+    var m = null, st = [], ep = [];
+    for (var i = 0; i < lines.length; i++) {
+      var line = lines[i].trim();
+      if (!line) continue;
+      var obj;
+      try { obj = JSON.parse(line); } catch (e) { continue; } // skip malformed
+      if (!obj || typeof obj !== "object") continue;
+      if (obj.type === "meta") { if (!m) m = obj; }
+      else if (obj.type === "step") { st.push(obj); }
+      else if (obj.type === "episode") { ep.push(obj); }
+    }
+    st.sort(function (a, b) { return (a.step|0) - (b.step|0); });
+    ep.sort(function (a, b) { return (a.ep|0) - (b.ep|0); });
+    return { meta: m, steps: st, episodes: ep };
+  }
+
+  function loadData(text, sourceName) {
+    var parsed = parseTelemetry(text);
+    if (!parsed.meta && parsed.steps.length === 0 && parsed.episodes.length === 0) {
+      loadHintEl.textContent = "no valid telemetry records found in " + (sourceName || "input");
+      return;
+    }
+    meta = parsed.meta || { profile: "unknown", drones: 0, area_w: 100, area_h: 100, victims: [] };
+    steps = parsed.steps;
+    episodes = parsed.episodes;
+
+    // reset playback / heatmap
+    coverageGrid = new Float32Array(GW * GH);
+    lastBuiltStep = -1;
+    pulses = [];
+    curStep = 0;
+    stepAccumulator = 0;
+    playing = false;
+    playBtn.textContent = "▶ Play";
+
+    scrub.min = 0;
+    scrub.max = Math.max(0, steps.length - 1);
+    scrub.value = 0;
+
+    var dc = meta.drones || (steps[0] && steps[0].drones ? steps[0].drones.length : 0);
+    subtitleEl.innerHTML = "profile <b>" + escapeHtml(String(meta.profile)) + "</b> · "
+      + "<b>" + dc + "</b> drones · "
+      + "area <b>" + fmt(meta.area_w) + "×" + fmt(meta.area_h) + "</b> m · "
+      + "<b>" + (meta.victims ? meta.victims.length : 0) + "</b> victims · "
+      + "<b>" + steps.length + "</b> replay steps · "
+      + "<b>" + episodes.length + "</b> episodes";
+    loadHintEl.textContent = "loaded " + (sourceName || "telemetry");
+
+    buildCoverageUpTo(0);
+    drawReplay();
+    drawMetrics();
+  }
+
+  function escapeHtml(s) {
+    return s.replace(/[&<>"']/g, function (c) {
+      return { "&": "&amp;", "<": "&lt;", ">": "&gt;", '"': "&quot;", "'": "&#39;" }[c];
+    });
+  }
+  function fmt(v) { return (typeof v === "number") ? (Math.round(v * 100) / 100) : v; }
+
+  // ---- Coordinate mapping (world metres -> canvas px), maintaining aspect ratio ----
+  function replayTransform() {
+    var W = replayCanvas.width, H = replayCanvas.height;
+    var pad = 28;
+    var aw = (meta && meta.area_w) || 100;
+    var ah = (meta && meta.area_h) || 100;
+    var availW = W - pad * 2, availH = H - pad * 2;
+    var scale = Math.min(availW / aw, availH / ah);
+    var drawW = aw * scale, drawH = ah * scale;
+    var offX = (W - drawW) / 2;
+    var offY = (H - drawH) / 2;
+    return {
+      scale: scale, offX: offX, offY: offY, drawW: drawW, drawH: drawH,
+      // world X -> screen X, world Y -> screen Y (Y grows downward on screen)
+      x: function (wx) { return offX + wx * scale; },
+      y: function (wy) { return offY + wy * scale; }
+    };
+  }
+
+  // ---- Coverage heatmap accumulation ----
+  function foldStepIntoGrid(rec) {
+    if (!rec || !rec.drones) return;
+    var aw = (meta && meta.area_w) || 100;
+    var ah = (meta && meta.area_h) || 100;
+    for (var i = 0; i < rec.drones.length; i++) {
+      var d = rec.drones[i];
+      var gx = Math.floor((d.x / aw) * GW);
+      var gy = Math.floor((d.y / ah) * GH);
+      if (gx < 0) gx = 0; if (gx >= GW) gx = GW - 1;
+      if (gy < 0) gy = 0; if (gy >= GH) gy = GH - 1;
+      // splat a small 3x3 footprint to suggest sensor swath
+      for (var ox = -1; ox <= 1; ox++) {
+        for (var oy = -1; oy <= 1; oy++) {
+          var cx = gx + ox, cy = gy + oy;
+          if (cx < 0 || cx >= GW || cy < 0 || cy >= GH) continue;
+          var w = (ox === 0 && oy === 0) ? 0.6 : 0.18;
+          var idx = cy * GW + cx;
+          var v = coverageGrid[idx] + w;
+          coverageGrid[idx] = v > 1 ? 1 : v;
+        }
+      }
+    }
+  }
+
+  // Rebuild heatmap so it reflects all steps 0..target (handles scrubbing backwards).
+  function buildCoverageUpTo(target) {
+    if (!coverageGrid) return;
+    if (target < lastBuiltStep) {
+      // scrubbed backwards — rebuild from scratch
+      coverageGrid.fill(0);
+      lastBuiltStep = -1;
+    }
+    for (var i = lastBuiltStep + 1; i <= target && i < steps.length; i++) {
+      foldStepIntoGrid(steps[i]);
+    }
+    if (target > lastBuiltStep) lastBuiltStep = Math.min(target, steps.length - 1);
+  }
+
+  // ---- Drawing: LEFT replay panel ----
+  function drawReplay() {
+    var W = replayCanvas.width, H = replayCanvas.height;
+    rctx.clearRect(0, 0, W, H);
+    rctx.fillStyle = "#04070a";
+    rctx.fillRect(0, 0, W, H);
+
+    var t = replayTransform();
+
+    // coverage heatmap (faint cyan cells)
+    if (coverageGrid) {
+      var cellW = t.drawW / GW, cellH = t.drawH / GH;
+      for (var gy = 0; gy < GH; gy++) {
+        for (var gx = 0; gx < GW; gx++) {
+          var v = coverageGrid[gy * GW + gx];
+          if (v <= 0) continue;
+          rctx.fillStyle = "rgba(46,230,230," + (0.07 + v * 0.34).toFixed(3) + ")";
+          rctx.fillRect(t.offX + gx * cellW, t.offY + gy * cellH, cellW + 0.5, cellH + 0.5);
+        }
+      }
+    }
+
+    // grid lines
+    rctx.strokeStyle = "rgba(70,120,130,0.18)";
+    rctx.lineWidth = 1;
+    var divisions = 8;
+    for (var i = 0; i <= divisions; i++) {
+      var fx = t.offX + (t.drawW * i / divisions);
+      var fy = t.offY + (t.drawH * i / divisions);
+      rctx.beginPath(); rctx.moveTo(fx, t.offY); rctx.lineTo(fx, t.offY + t.drawH); rctx.stroke();
+      rctx.beginPath(); rctx.moveTo(t.offX, fy); rctx.lineTo(t.offX + t.drawW, fy); rctx.stroke();
+    }
+
+    // area border
+    rctx.strokeStyle = "rgba(46,230,230,0.6)";
+    rctx.lineWidth = 1.5;
+    rctx.strokeRect(t.offX, t.offY, t.drawW, t.drawH);
+
+    // axis labels
+    rctx.fillStyle = "#5b7178";
+    rctx.font = "10px monospace";
+    rctx.textAlign = "left";
+    rctx.fillText("0", t.offX + 2, t.offY + t.drawH + 12);
+    rctx.textAlign = "right";
+    rctx.fillText(fmt(meta ? meta.area_w : 0) + "m (x)", t.offX + t.drawW, t.offY + t.drawH + 12);
+    rctx.save();
+    rctx.translate(t.offX - 6, t.offY + t.drawH);
+    rctx.rotate(-Math.PI / 2);
+    rctx.textAlign = "left";
+    rctx.fillText(fmt(meta ? meta.area_h : 0) + "m (y)", 0, 0);
+    rctx.restore();
+
+    // victims
+    if (meta && meta.victims) {
+      for (var v = 0; v < meta.victims.length; v++) {
+        var vx = t.x(meta.victims[v][0]), vy = t.y(meta.victims[v][1]);
+        rctx.strokeStyle = "#ff5a5a";
+        rctx.lineWidth = 2;
+        var s = 7;
+        rctx.beginPath();
+        rctx.moveTo(vx - s, vy); rctx.lineTo(vx + s, vy);
+        rctx.moveTo(vx, vy - s); rctx.lineTo(vx, vy + s);
+        rctx.stroke();
+        rctx.beginPath();
+        rctx.arc(vx, vy, s + 2, 0, Math.PI * 2);
+        rctx.strokeStyle = "rgba(255,90,90,0.5)";
+        rctx.lineWidth = 1;
+        rctx.stroke();
+        rctx.fillStyle = "#ff8a8a";
+        rctx.font = "10px monospace";
+        rctx.textAlign = "left";
+        rctx.fillText("victim " + v, vx + s + 4, vy - 4);
+      }
+    }
+
+    // detection pulses (expanding rings)
+    for (var p = pulses.length - 1; p >= 0; p--) {
+      var pu = pulses[p];
+      var px = t.x(pu.wx), py = t.y(pu.wy);
+      var r = 6 + pu.age * 40;
+      var alpha = 1 - pu.age;
+      if (alpha <= 0) { pulses.splice(p, 1); continue; }
+      rctx.beginPath();
+      rctx.arc(px, py, r, 0, Math.PI * 2);
+      rctx.strokeStyle = "rgba(67,224,122," + (alpha * 0.8).toFixed(3) + ")";
+      rctx.lineWidth = 2;
+      rctx.stroke();
+    }
+
+    // drones
+    var rec = steps[curStep];
+    var activeDetections = 0;
+    if (rec && rec.drones) {
+      for (var di = 0; di < rec.drones.length; di++) {
+        var d = rec.drones[di];
+        var dx = t.x(d.x), dy = t.y(d.y);
+        var detecting = !!d.det;
+        if (detecting) activeDetections++;
+
+        // oriented triangle along hdg (screen Y down => use hdg directly)
+        var hdg = (typeof d.hdg === "number") ? d.hdg : 0;
+        var size = 9;
+        var col = detecting ? "#b6ff3c" : "#2ee6e6";
+        rctx.save();
+        rctx.translate(dx, dy);
+        rctx.rotate(hdg);
+        rctx.beginPath();
+        rctx.moveTo(size, 0);
+        rctx.lineTo(-size * 0.7, size * 0.6);
+        rctx.lineTo(-size * 0.4, 0);
+        rctx.lineTo(-size * 0.7, -size * 0.6);
+        rctx.closePath();
+        rctx.fillStyle = col;
+        rctx.globalAlpha = detecting ? 1 : 0.92;
+        rctx.fill();
+        rctx.globalAlpha = 1;
+        if (detecting) {
+          rctx.strokeStyle = "rgba(182,255,60,0.9)";
+          rctx.lineWidth = 1;
+          rctx.stroke();
+        }
+        rctx.restore();
+
+        // id label
+        rctx.fillStyle = col;
+        rctx.font = "10px monospace";
+        rctx.textAlign = "center";
+        rctx.fillText(String(d.id), dx, dy - 13);
+
+        // battery bar under drone
+        var bw = 18, bh = 3;
+        var bx = dx - bw / 2, by = dy + 11;
+        var batt = (typeof d.batt === "number") ? Math.max(0, Math.min(100, d.batt)) : 0;
+        rctx.fillStyle = "rgba(255,255,255,0.12)";
+        rctx.fillRect(bx, by, bw, bh);
+        // green -> red interpolation by battery
+        var g = Math.round(2.24 * batt);          // 0..224
+        var rr = Math.round(255 - 1.9 * batt);    // 255..65
+        rctx.fillStyle = "rgb(" + rr + "," + g + ",60)";
+        rctx.fillRect(bx, by, bw * (batt / 100), bh);
+      }
+    }
+
+    // step readout
+    var cov = rec && typeof rec.coverage === "number" ? rec.coverage : 0;
+    var total = steps.length;
+    if (total === 0) {
+      replayReadout.innerHTML = '<span class="warn">no replay steps in telemetry</span>';
+    } else {
+      replayReadout.textContent =
+        "step " + (curStep + 1) + "/" + total +
+        " · ep " + (rec ? rec.ep : "—") +
+        " · t=" + (rec && typeof rec.t === "number" ? rec.t.toFixed(2) : "—") +
+        " · coverage " + (cov * 100).toFixed(1) + "%" +
+        " · active detections " + activeDetections;
+    }
+  }
+
+  // ---- Drawing: RIGHT metrics panel ----
+  function lineChart(x, y, w, h, title, color, values) {
+    // axes box
+    mctx.strokeStyle = "rgba(70,120,130,0.4)";
+    mctx.lineWidth = 1;
+    mctx.strokeRect(x, y, w, h);
+
+    mctx.fillStyle = color;
+    mctx.font = "11px monospace";
+    mctx.textAlign = "left";
+    mctx.fillText(title, x + 4, y - 5);
+
+    if (!values || values.length === 0) {
+      mctx.fillStyle = "#5b7178";
+      mctx.fillText("(no data)", x + w / 2 - 28, y + h / 2);
+      return;
+    }
+
+    var min = Infinity, max = -Infinity;
+    for (var i = 0; i < values.length; i++) {
+      var v = values[i];
+      if (typeof v !== "number" || !isFinite(v)) continue;
+      if (v < min) min = v;
+      if (v > max) max = v;
+    }
+    if (!isFinite(min)) { min = 0; max = 1; }
+    if (min === max) { min -= 1; max += 1; }
+    var range = max - min;
+
+    var n = values.length;
+    function px(i) { return x + (n === 1 ? w / 2 : (i / (n - 1)) * w); }
+    function py(v) { return y + h - ((v - min) / range) * h; }
+
+    // zero line if it falls within range
+    if (min < 0 && max > 0) {
+      var zy = py(0);
+      mctx.strokeStyle = "rgba(120,140,150,0.25)";
+      mctx.setLineDash([3, 3]);
+      mctx.beginPath(); mctx.moveTo(x, zy); mctx.lineTo(x + w, zy); mctx.stroke();
+      mctx.setLineDash([]);
+    }
+
+    // the line
+    mctx.strokeStyle = color;
+    mctx.lineWidth = 1.6;
+    mctx.beginPath();
+    var started = false;
+    for (var j = 0; j < n; j++) {
+      var vv = values[j];
+      if (typeof vv !== "number" || !isFinite(vv)) continue;
+      var X = px(j), Y = py(vv);
+      if (!started) { mctx.moveTo(X, Y); started = true; }
+      else mctx.lineTo(X, Y);
+    }
+    mctx.stroke();
+
+    // latest marker dot
+    var lastV = values[n - 1];
+    if (typeof lastV === "number" && isFinite(lastV)) {
+      mctx.fillStyle = color;
+      mctx.beginPath();
+      mctx.arc(px(n - 1), py(lastV), 3.2, 0, Math.PI * 2);
+      mctx.fill();
+    }
+
+    // min/max annotations
+    mctx.fillStyle = "#5b7178";
+    mctx.font = "9px monospace";
+    mctx.textAlign = "right";
+    mctx.fillText(fmtNum(max), x + w - 3, y + 10);
+    mctx.fillText(fmtNum(min), x + w - 3, y + h - 3);
+    // episode axis labels
+    mctx.textAlign = "left";
+    mctx.fillText("ep 0", x + 2, y + h + 11);
+    mctx.textAlign = "right";
+    mctx.fillText("ep " + (n - 1), x + w, y + h + 11);
+  }
+
+  function fmtNum(v) {
+    if (!isFinite(v)) return "—";
+    var a = Math.abs(v);
+    if (a >= 1000) return v.toFixed(0);
+    if (a >= 1) return v.toFixed(1);
+    return v.toFixed(3);
+  }
+
+  function drawMetrics() {
+    var W = metricsCanvas.width, H = metricsCanvas.height;
+    mctx.clearRect(0, 0, W, H);
+    mctx.fillStyle = "#04070a";
+    mctx.fillRect(0, 0, W, H);
+
+    // legend
+    mctx.font = "10px monospace";
+    mctx.textAlign = "left";
+    var legend = [["mean return", "#43e07a"], ["policy loss", "#f6a13c"], ["value loss", "#ff5a5a"]];
+    var lx = 14;
+    for (var l = 0; l < legend.length; l++) {
+      mctx.fillStyle = legend[l][1];
+      mctx.fillRect(lx, 8, 9, 9);
+      mctx.fillStyle = "#cfe9ec";
+      mctx.fillText(legend[l][0], lx + 13, 16);
+      lx += mctx.measureText(legend[l][0]).width + 36;
+    }
+
+    var ret = episodes.map(function (e) { return e.mean_return; });
+    var pol = episodes.map(function (e) { return e.policy_loss; });
+    var val = episodes.map(function (e) { return e.value_loss; });
+
+    var marginL = 14, marginR = 14, top = 38, gap = 30;
+    var chartW = W - marginL - marginR;
+    var chartH = (H - top - gap * 3) / 3;
+
+    var y0 = top;
+    lineChart(marginL, y0, chartW, chartH, "mean return", "#43e07a", ret);
+    var y1 = y0 + chartH + gap;
+    lineChart(marginL, y1, chartW, chartH, "policy loss", "#f6a13c", pol);
+    var y2 = y1 + chartH + gap;
+    lineChart(marginL, y2, chartW, chartH, "value loss (autoscaled)", "#ff5a5a", val);
+
+    if (episodes.length === 0) {
+      metricsReadout.innerHTML = '<span class="warn">no episode metrics in telemetry</span>';
+    } else {
+      var last = episodes[episodes.length - 1];
+      var found = 0;
+      for (var i = 0; i < episodes.length; i++) {
+        if (typeof episodes[i].victims_found === "number" && episodes[i].victims_found > found)
+          found = episodes[i].victims_found;
+      }
+      metricsReadout.textContent =
+        episodes.length + " episodes · latest ep " + last.ep +
+        " · return " + fmtNum(last.mean_return) +
+        " · policy " + fmtNum(last.policy_loss) +
+        " · value " + fmtNum(last.value_loss) +
+        " · max victims found " + found;
+    }
+  }
+
+  // ---- Playback loop ----
+  function frame(now) {
+    if (playing && steps.length > 1) {
+      if (!lastFrameTime) lastFrameTime = now;
+      var dt = (now - lastFrameTime) / 1000;
+      lastFrameTime = now;
+      var speed = parseFloat(speedSel.value) || 1;
+      var stepsPerSec = 6 * speed; // base playback rate
+      stepAccumulator += dt * stepsPerSec;
+      while (stepAccumulator >= 1) {
+        stepAccumulator -= 1;
+        advanceStep(1);
+        if (curStep >= steps.length - 1) {
+          curStep = steps.length - 1;
+          playing = false;
+          playBtn.textContent = "▶ Play";
+          break;
+        }
+      }
+    } else {
+      lastFrameTime = now;
+    }
+
+    // age pulses
+    for (var i = 0; i < pulses.length; i++) pulses[i].age += 0.03;
+
+    drawReplay();
+    requestAnimationFrame(frame);
+  }
+
+  function advanceStep(delta) {
+    var prev = curStep;
+    curStep += delta;
+    if (curStep < 0) curStep = 0;
+    if (curStep > steps.length - 1) curStep = steps.length - 1;
+    scrub.value = curStep;
+    buildCoverageUpTo(curStep);
+    spawnPulsesForStep(curStep);
+  }
+
+  function spawnPulsesForStep(idx) {
+    var rec = steps[idx];
+    if (!rec || !rec.drones) return;
+    for (var i = 0; i < rec.drones.length; i++) {
+      var d = rec.drones[i];
+      if (d.det) pulses.push({ wx: d.x, wy: d.y, age: 0 });
+    }
+  }
+
+  // ---- Controls wiring ----
+  playBtn.addEventListener("click", function () {
+    if (steps.length <= 1) return;
+    playing = !playing;
+    playBtn.textContent = playing ? "❚❚ Pause" : "▶ Play";
+    if (playing && curStep >= steps.length - 1) {
+      // restart from beginning
+      curStep = 0;
+      coverageGrid && coverageGrid.fill(0);
+      lastBuiltStep = -1;
+      pulses = [];
+      buildCoverageUpTo(0);
+      scrub.value = 0;
+    }
+    lastFrameTime = 0;
+  });
+
+  scrub.addEventListener("input", function () {
+    playing = false;
+    playBtn.textContent = "▶ Play";
+    curStep = parseInt(scrub.value, 10) || 0;
+    buildCoverageUpTo(curStep);
+    spawnPulsesForStep(curStep);
+    drawReplay();
+  });
+
+  speedSel.addEventListener("change", function () { lastFrameTime = 0; });
+
+  fileInput.addEventListener("change", function (ev) {
+    var f = ev.target.files && ev.target.files[0];
+    if (!f) return;
+    var reader = new FileReader();
+    reader.onload = function () { loadData(String(reader.result), f.name); };
+    reader.onerror = function () { loadHintEl.textContent = "could not read file"; };
+    reader.readAsText(f);
+  });
+
+  // drag & drop onto the page
+  window.addEventListener("dragover", function (e) { e.preventDefault(); });
+  window.addEventListener("drop", function (e) {
+    e.preventDefault();
+    var f = e.dataTransfer && e.dataTransfer.files && e.dataTransfer.files[0];
+    if (!f) return;
+    var reader = new FileReader();
+    reader.onload = function () { loadData(String(reader.result), f.name); };
+    reader.readAsText(f);
+  });
+
+  // ---- Auto-fetch bundled sample (graceful on file:// CORS failure) ----
+  function tryAutoFetch() {
+    if (typeof fetch !== "function") {
+      loadHintEl.textContent = "drop a .jsonl file or use the picker";
+      return;
+    }
+    fetch("sample_telemetry.jsonl")
+      .then(function (r) {
+        if (!r.ok) throw new Error("status " + r.status);
+        return r.text();
+      })
+      .then(function (text) { loadData(text, "sample_telemetry.jsonl"); })
+      .catch(function () {
+        loadHintEl.textContent = "auto-load blocked (file://) — drop a .jsonl file or use the picker";
+        // draw empty frames so canvases aren't blank
+        drawReplay();
+        drawMetrics();
+      });
+  }
+
+  // boot
+  drawReplay();
+  drawMetrics();
+  tryAutoFetch();
+  requestAnimationFrame(frame);
+})();
+</script>
+</body>
+</html>