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feat(swarm): real Candle autodiff PPO + A-MAPPO role attention + GPU training (M4) 

Replaces the finite-difference PPO placeholder with a real GPU-capable Candle
0.9 autodiff trainer, adds A-MAPPO heterogeneous-role attention, a runnable
training binary, and right-sized GCP/local launch scripts. This is the unlock
that makes "GPU long training cycles" actually mean something — the previous
ppo_update did no gradient descent.

## Real autodiff PPO (feature `train`, optional `cuda`)
- candle_ppo.rs: CandleActorCritic (64→128→64 MLP + action/value heads +
  learnable log_std), CandlePpoConfig, CandleTrainer with GAE and a genuine
  optimizer.backward_step over the network. select_device() picks CUDA when
  built --features cuda and a GPU is present, else CPU.
- Verified: 5-episode CPU smoke run shows value_loss 12643→12375 (critic
  actually learning); safetensors checkpoint saved. Placeholder never moved weights.

## A-MAPPO heterogeneous-role attention (role_attention.rs, always compiled)
Addresses the four sensor-vs-relay edge cases:
- relay attention floor (prevents collapse — relays produce no CSI)
- role-segmented sensor/relay attention pools (variable neighbor cardinality)
- sensor-gated triangulation-geometry penalty (protects 3-view fusion baseline,
  ADR-148 §4.2 — relays not dragged into triangulation geometry)
- one-hot role embeddings for keys

## Training binary
- src/bin/train_marl.rs (required-features=["train"], excluded from default build)
- CLI: --episodes --drones --profile --steps --checkpoint-dir --checkpoint-every
- Wires CandleTrainer to the SwarmOrchestrator rollout loop; GAE + PPO update
  per episode; periodic safetensors checkpoints

## Right-sized launch (scripts/gcp/)
- provision_marl.sh: g2-standard-16 (1× L4, 16 vCPU, ~$1.40/hr) — NOT the
  $29/hr A100×8 box. MARL is rollout-bound not matmul-bound; ~21× cheaper.
- run_marl_train.sh: GCP rsync + train + checkpoint pull
- run_marl_train_local.sh: local RTX 5080, $0
- A100×8 provision_training.sh left for OccWorld (which saturates the GPUs)

## Tests
- --no-default-features: 91/91 (87 + 4 role_attention)
- --features train: 96/96 (+ 5 candle_ppo, incl. real-autodiff verification)
- --features ruflo,itar-unrestricted: 104/104
- default build stays light: train_marl excluded via required-features

Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
ruv 2026-05-30 12:43:56 -04:00
parent f2bd035a22
commit 4f004e018b
9 changed files with 1067 additions and 0 deletions
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199
scripts/gcp/provision_marl.sh Executable file
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#!/usr/bin/env bash
# Provision GCP L4 instance for ruview-swarm MARL training (ADR-148 M4).
#
# RIGHT-SIZING RATIONALE:
# The MARL policy is a 64→128→64 MLP (~12K params). GPU matmul is NOT the
# bottleneck — environment-rollout throughput (stepping the swarm sim) is.
# An L4 + 16 vCPU (g2-standard-16, ~$1.40/hr) beats an 8× A100 box
# (a2-highgpu-8g, ~$29/hr) for this workload at 1/20th the cost.
# Reserve the A100×8 box (provision_training.sh) for OccWorld world-model
# training, which actually saturates the GPUs.
#
# Usage: bash scripts/gcp/provision_marl.sh [--dry-run]
#
# Provisions a g2-standard-16 (1× L4 24GB, 16 vCPU) in us-central1-a
# (fallback us-east1-b).
# GCP project: cognitum-20260110
# Auth: ruv@ruv.net (gcloud must already be authenticated)
set -euo pipefail
# ── Constants ──────────────────────────────────────────────────────────────────
PROJECT="cognitum-20260110"
INSTANCE_NAME="ruview-marl-$(date +%Y%m%d)"
MACHINE_TYPE="g2-standard-16"
PRIMARY_ZONE="us-central1-a"
FALLBACK_ZONE="us-east1-b"
IMAGE_FAMILY="pytorch-latest-gpu"
IMAGE_PROJECT="deeplearning-platform-release"
DISK_SIZE="200GB"
DISK_TYPE="pd-ssd"
# Cost reference: g2-standard-16 ~$1.40/hr on-demand (us-central1, 2026).
# Compare a2-highgpu-8g at ~$29.39/hr — a ~20× cost reduction. MARL is
# rollout-bound (CPU-stepped swarm sim), not matmul-bound, so the 16 vCPUs
# matter more than peak GPU FLOPs for this 12K-param policy.
COST_PER_HR="1.40"
A100_BOX_RATE="29.39"
# Rough estimate: 5000 episodes × 4 drones, rollout-bound on 16 vCPU ≈ 2–4 hr.
RUN_HOURS="3"
# ── Flags ─────────────────────────────────────────────────────────────────────
DRY_RUN=false
for arg in "$@"; do
case "$arg" in
--dry-run) DRY_RUN=true ;;
-h|--help)
echo "Usage: $0 [--dry-run]"
echo " --dry-run Echo gcloud commands without executing them"
exit 0
;;
*)
echo "Unknown argument: $arg" >&2
echo "Usage: $0 [--dry-run]" >&2
exit 1
;;
esac
done
# ── Helpers ───────────────────────────────────────────────────────────────────
run() {
if [[ "$DRY_RUN" == "true" ]]; then
echo "[DRY-RUN] $*"
else
"$@"
fi
}
log() { echo "[provision_marl] $*"; }
# ── Startup script (embedded heredoc) ─────────────────────────────────────────
# Written to a temp file so gcloud can reference it via --metadata-from-file.
# For MARL the heavy lifting is a Rust/Candle binary, so we install the Rust
# toolchain rather than a conda Python env.
STARTUP_SCRIPT_FILE="$(mktemp /tmp/startup_marl_XXXXXX.sh)"
trap 'rm -f "$STARTUP_SCRIPT_FILE"' EXIT
cat > "$STARTUP_SCRIPT_FILE" << 'STARTUP_EOF'
#!/usr/bin/env bash
set -euo pipefail
LOGFILE="/var/log/ruview-marl-startup.log"
exec > >(tee -a "$LOGFILE") 2>&1
echo "[startup] $(date): beginning MARL environment setup"
# ── 1. System packages ────────────────────────────────────────────────────────
apt-get update -qq
apt-get install -y -qq git rsync wget curl htop nvtop screen tmux \
build-essential pkg-config libssl-dev
# ── 2. Rust toolchain (for cargo build of ruview-swarm) ────────────────────────
TARGET_USER="$(logname 2>/dev/null || echo user)"
TARGET_HOME="$(getent passwd "$TARGET_USER" | cut -d: -f6)"
if [[ ! -d "$TARGET_HOME/.cargo" ]]; then
echo "[startup] Installing Rust toolchain for $TARGET_USER ..."
sudo -u "$TARGET_USER" bash -c \
'curl --proto "=https" --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y'
fi
# ── 3. CUDA sanity (deeplearning image ships CUDA 12 + driver) ─────────────────
echo "[startup] CUDA check:"
nvidia-smi || echo "[startup] WARNING: nvidia-smi not available yet"
# ── 4. Checkpoint dirs + repo sync placeholder ─────────────────────────────────
# Actual crate sync is done by run_marl_train.sh via rsync before the build.
sudo -u "$TARGET_USER" mkdir -p "$TARGET_HOME/ruview-swarm" \
"$TARGET_HOME/marl-checkpoints"
echo "[startup] $(date): setup complete — instance ready for MARL training"
STARTUP_EOF
# ── L4 availability check (with zone fallback) ─────────────────────────────────
ZONE="$PRIMARY_ZONE"
if [[ "$DRY_RUN" == "false" ]]; then
log "Checking L4 availability in $PRIMARY_ZONE ..."
AVAIL=$(gcloud compute accelerator-types list \
--project="$PROJECT" \
--filter="name=nvidia-l4 AND zone=$PRIMARY_ZONE" \
--format="value(name)" 2>/dev/null | head -1)
if [[ -z "$AVAIL" ]]; then
log "L4 not available in $PRIMARY_ZONE — falling back to $FALLBACK_ZONE"
ZONE="$FALLBACK_ZONE"
else
log "L4 confirmed available in $PRIMARY_ZONE"
fi
else
log "[DRY-RUN] Would check L4 availability in $PRIMARY_ZONE (fallback: $FALLBACK_ZONE)"
fi
# ── Cost estimate ──────────────────────────────────────────────────────────────
TOTAL_COST=$(awk "BEGIN {printf \"%.2f\", $COST_PER_HR * $RUN_HOURS}")
A100_COST=$(awk "BEGIN {printf \"%.2f\", $A100_BOX_RATE * $RUN_HOURS}")
SAVINGS=$(awk "BEGIN {printf \"%.0f\", $A100_BOX_RATE / $COST_PER_HR}")
log "Cost estimate:"
log " Machine type : $MACHINE_TYPE (1× L4 24GB, 16 vCPU)"
log " Rate : ~\$$COST_PER_HR/hr (on-demand, $ZONE)"
log " Est. duration: ~${RUN_HOURS} hr (5000 episodes, rollout-bound)"
log " Est. total : ~\$$TOTAL_COST"
log " vs A100×8 : ~\$$A100_COST for the same wall time (~${SAVINGS}× more expensive)"
log " Why L4 : MARL policy is a 12K-param MLP — bottleneck is CPU env rollout, not GPU matmul"
log " Tip: Use --preemptible to cut cost further at the risk of interruptions"
# ── Provision instance ────────────────────────────────────────────────────────
log "Provisioning $INSTANCE_NAME in $ZONE ..."
run gcloud compute instances create "$INSTANCE_NAME" \
--project="$PROJECT" \
--zone="$ZONE" \
--machine-type="$MACHINE_TYPE" \
--accelerator="type=nvidia-l4,count=1" \
--image-family="$IMAGE_FAMILY" \
--image-project="$IMAGE_PROJECT" \
--boot-disk-size="$DISK_SIZE" \
--boot-disk-type="$DISK_TYPE" \
--boot-disk-device-name="${INSTANCE_NAME}-disk" \
--maintenance-policy=TERMINATE \
--restart-on-failure \
--metadata-from-file="startup-script=$STARTUP_SCRIPT_FILE" \
--scopes="cloud-platform" \
--format="value(name)"
if [[ "$DRY_RUN" == "true" ]]; then
log "[DRY-RUN] Skipping IP lookup and SSH command output"
exit 0
fi
# ── Wait for instance to be ready ─────────────────────────────────────────────
log "Waiting for instance to reach RUNNING state ..."
for i in $(seq 1 30); do
STATUS=$(gcloud compute instances describe "$INSTANCE_NAME" \
--project="$PROJECT" --zone="$ZONE" \
--format="value(status)" 2>/dev/null || echo "UNKNOWN")
if [[ "$STATUS" == "RUNNING" ]]; then
break
fi
sleep 10
if [[ $i -eq 30 ]]; then
log "ERROR: Instance did not reach RUNNING within 5 min" >&2
exit 1
fi
done
# ── Print connection info ─────────────────────────────────────────────────────
INSTANCE_IP=$(gcloud compute instances describe "$INSTANCE_NAME" \
--project="$PROJECT" --zone="$ZONE" \
--format="value(networkInterfaces[0].accessConfigs[0].natIP)")
log "Instance ready:"
log " Name : $INSTANCE_NAME"
log " Zone : $ZONE"
log " IP : $INSTANCE_IP"
log " SSH : gcloud compute ssh $INSTANCE_NAME --project=$PROJECT --zone=$ZONE"
log " SSH IP : ssh $(gcloud config get-value account 2>/dev/null)@$INSTANCE_IP"
log ""
log "Startup script is running in background (/var/log/ruview-marl-startup.log)."
log "Wait 2-3 min for the Rust toolchain install before running run_marl_train.sh."
log ""
log "Next step:"
log " bash scripts/gcp/run_marl_train.sh $INSTANCE_IP"
log "Teardown when done:"
log " bash scripts/gcp/teardown.sh $INSTANCE_NAME"

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#!/usr/bin/env bash
# Run ruview-swarm MARL training on a GCP L4 instance (ADR-148 M4).
# Usage: bash scripts/gcp/run_marl_train.sh <INSTANCE_IP> [EPISODES] [DRONES] [PROFILE]
#
# Rsyncs the v2/ Rust workspace to the instance, then runs the Candle PPO
# MARL trainer:
# cargo run --release -p ruview-swarm --features train,cuda --bin train_marl
# Downloads the trained checkpoints back on completion.
#
# NOTE: the `--bin train_marl` target is added by the companion MARL trainer
# work (Candle PPO trainer). This script calls it; it is expected to
# exist once that work lands.
set -euo pipefail
# ── Usage ─────────────────────────────────────────────────────────────────────
if [[ $# -lt 1 ]]; then
echo "Usage: $0 <INSTANCE_IP> [EPISODES] [DRONES] [PROFILE]" >&2
echo ""
echo " INSTANCE_IP External IP of the GCP L4 MARL training instance"
echo " EPISODES Training episodes (default: 5000)"
echo " DRONES Swarm size (default: 4)"
echo " PROFILE Mission profile (default: sar)"
echo ""
echo "Example:"
echo " $0 34.123.45.67"
echo " $0 34.123.45.67 10000 6 sar"
exit 1
fi
INSTANCE_IP="$1"
EPISODES="${2:-5000}"
DRONES="${3:-4}"
PROFILE="${4:-sar}"
GCP_USER="${GCP_USER:-$(gcloud config get-value account 2>/dev/null | cut -d@ -f1)}"
REMOTE="${GCP_USER}@${INSTANCE_IP}"
LOCAL_V2_DIR="$(cd "$(dirname "$0")/../.." && pwd)/v2"
OUTPUT_DIR="./out/gcp-checkpoints/marl"
REMOTE_CRATE="~/ruview-swarm"
REMOTE_CHECKPOINTS="~/ruview-swarm/marl-checkpoints"
log() { echo "[run_marl_train] $*"; }
# ── Validation ────────────────────────────────────────────────────────────────
if [[ ! -d "$LOCAL_V2_DIR" ]]; then
echo "ERROR: v2 workspace not found: $LOCAL_V2_DIR" >&2
exit 1
fi
log "Config: $EPISODES episodes, $DRONES drones, profile=$PROFILE"
# ── SSH connectivity check ────────────────────────────────────────────────────
SSH_OPTS="-o StrictHostKeyChecking=no -o ConnectTimeout=15 -o BatchMode=yes"
log "Checking SSH connectivity to $REMOTE ..."
if ! ssh $SSH_OPTS "$REMOTE" "echo ok" &>/dev/null; then
echo "ERROR: Cannot SSH to $REMOTE" >&2
echo " Ensure the instance is running and your SSH key is authorized." >&2
echo " Try: gcloud compute ssh <INSTANCE_NAME> --project=cognitum-20260110" >&2
exit 1
fi
log "SSH connection OK"
# ── Startup script completion check ───────────────────────────────────────────
log "Checking that startup script completed ..."
STARTUP_READY=$(ssh $SSH_OPTS "$REMOTE" \
"grep -c 'setup complete' /var/log/ruview-marl-startup.log 2>/dev/null || echo 0")
if [[ "$STARTUP_READY" -lt 1 ]]; then
log "WARNING: Startup script may not have finished yet."
log " Check /var/log/ruview-marl-startup.log on the instance."
log " Continuing anyway — the Rust toolchain may need more time."
fi
# ── Rsync the v2 Rust workspace ───────────────────────────────────────────────
# Exclude build artifacts and VCS — the instance rebuilds from source.
log "Rsyncing v2 workspace → $REMOTE:$REMOTE_CRATE ..."
ssh $SSH_OPTS "$REMOTE" "mkdir -p $REMOTE_CRATE"
rsync -avz --progress --stats \
-e "ssh $SSH_OPTS" \
--exclude="target/" \
--exclude=".git/" \
--exclude="marl-checkpoints/" \
--exclude="*.log" \
"$LOCAL_V2_DIR/" \
"${REMOTE}:${REMOTE_CRATE}/"
log "Workspace sync complete"
# ── Run MARL training ─────────────────────────────────────────────────────────
log "=== MARL training ($EPISODES episodes, $DRONES drones, $PROFILE) ==="
TRAIN_START=$(date +%s)
ssh $SSH_OPTS "$REMOTE" bash << REMOTE_TRAIN
set -euo pipefail
# shellcheck source=/dev/null
source "\$HOME/.cargo/env"
cd "\$HOME/ruview-swarm"
mkdir -p ./marl-checkpoints
echo "[train] \$(date): starting Candle PPO MARL trainer"
# --bin train_marl is provided by the companion MARL trainer work.
cargo run --release -p ruview-swarm --features train,cuda --bin train_marl -- \\
--episodes ${EPISODES} --drones ${DRONES} --profile ${PROFILE} \\
--checkpoint-dir ./marl-checkpoints
echo "[train] \$(date): MARL training complete"
ls -lh ./marl-checkpoints/
REMOTE_TRAIN
TRAIN_END=$(date +%s)
TRAIN_MIN=$(( (TRAIN_END - TRAIN_START) / 60 ))
log "Training complete in ${TRAIN_MIN} min"
# ── Download checkpoints ──────────────────────────────────────────────────────
log "Downloading checkpoints → $OUTPUT_DIR ..."
mkdir -p "$OUTPUT_DIR"
rsync -avz --progress --stats \
-e "ssh $SSH_OPTS" \
"${REMOTE}:${REMOTE_CHECKPOINTS}/" \
"$OUTPUT_DIR/"
# ── Verify download ───────────────────────────────────────────────────────────
LOCAL_FILE_COUNT=$(find "$OUTPUT_DIR" -type f 2>/dev/null | wc -l)
LOCAL_SIZE_MB=$(du -sm "$OUTPUT_DIR" 2>/dev/null | awk '{print $1}')
log "Downloaded $LOCAL_FILE_COUNT files, ~${LOCAL_SIZE_MB} MB to $OUTPUT_DIR"
if [[ "$LOCAL_FILE_COUNT" -lt 1 ]]; then
echo "WARNING: No checkpoints were downloaded from $REMOTE" >&2
fi
# ── Summary ───────────────────────────────────────────────────────────────────
TRAIN_HR=$(awk "BEGIN {printf \"%.2f\", $TRAIN_MIN / 60}")
COST=$(awk "BEGIN {printf \"%.2f\", 1.40 * $TRAIN_HR}")
log ""
log "=== MARL training complete ==="
log " Episodes : $EPISODES (drones=$DRONES, profile=$PROFILE)"
log " Wall time : ${TRAIN_MIN} min (${TRAIN_HR} hr)"
log " Est. compute cost: ~\$$COST (at \$1.40/hr on-demand, g2-standard-16)"
log " Checkpoints in : $OUTPUT_DIR"
log ""
log "Next step (teardown):"
log " bash scripts/gcp/teardown.sh <INSTANCE_NAME> --skip-download"

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scripts/gcp/run_marl_train_local.sh Executable file
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#!/usr/bin/env bash
# Run ruview-swarm MARL training locally on the RTX 5080 (no GCP needed).
# For development runs and smaller episode counts. The local 5080 (16GB) is
# more than enough for the 64→128→64 policy network.
#
# Usage: bash scripts/gcp/run_marl_train_local.sh [EPISODES] [DRONES] [PROFILE]
#
# NOTE: the `--bin train_marl` target is added by the companion MARL trainer
# work (Candle PPO trainer). This script calls it.
set -euo pipefail
cd "$(dirname "$0")/../../v2"
EPISODES="${1:-1000}"
DRONES="${2:-4}"
PROFILE="${3:-sar}"
echo "Training MARL: $EPISODES episodes, $DRONES drones, profile=$PROFILE on local GPU"
cargo run --release -p ruview-swarm --features train,cuda --bin train_marl -- \
--episodes "$EPISODES" --drones "$DRONES" --profile "$PROFILE" \
--checkpoint-dir ./marl-checkpoints 2>&1 | tee marl-train-$(date +%Y%m%d-%H%M%S).log

2
v2/Cargo.lock generated
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@ -7461,6 +7461,8 @@ name = "ruview-swarm"
version = "0.1.0"
dependencies = [
"async-trait",
"candle-core 0.9.2",
"candle-nn 0.9.2",
"criterion",
"hmac",
"mavlink",

14
v2/crates/ruview-swarm/Cargo.toml
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@ -17,6 +17,10 @@ simulation = []
demo = ["simulation"]
full = ["mavlink", "onnx", "demo", "itar-unrestricted"]
ruflo = ["dep:reqwest", "dep:serde_json"]
# Heavy GPU-capable MARL training (real Candle autodiff PPO). Off by default so
# the default build stays light and the existing test suite keeps passing.
train = ["dep:candle-core", "dep:candle-nn"]
cuda = ["candle-core/cuda", "candle-nn/cuda"]
[dependencies]
wifi-densepose-core = { path = "../wifi-densepose-core" }
@ -36,6 +40,10 @@ mavlink = { version = "0.13", optional = true }
# ONNX Runtime (optional — for MARL actor inference)
ort = { version = "2.0.0-rc.11", optional = true }
# Candle 0.9 — real autodiff PPO training (optional, behind `train` feature).
candle-core = { version = "0.9", default-features = false, optional = true }
candle-nn = { version = "0.9", default-features = false, optional = true }
# HTTP client (optional — for Ruflo HTTP backend)
reqwest = { version = "0.12", features = ["json"], optional = true }
@ -60,3 +68,9 @@ tokio-test = "0.4"
[[bench]]
name = "swarm_bench"
harness = false
# MARL training binary — requires the `train` feature (Candle autodiff).
# Excluded from the default build so `cargo test`/CI stay light.
[[bin]]
name = "train_marl"
required-features = ["train"]

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v2/crates/ruview-swarm/src/bin/train_marl.rs Normal file
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//! MARL training entry point for ruview-swarm (ADR-148 M4).
//!
//! Real Candle autodiff PPO training loop. Runs on CPU, or CUDA when built
//! with `--features train,cuda` (local RTX 5080 or a GCP L4 instance).
//!
//! Usage:
//! cargo run --release -p ruview-swarm --features train,cuda --bin train_marl -- \
//! --episodes 5000 --drones 4 --profile sar --checkpoint-dir ./marl-checkpoints
//!
//! Right-sizing note: the policy is a 64→128→64 MLP. The bottleneck is
//! environment-rollout throughput, not GPU matmul — an L4 + 16 vCPU beats an
//! 8× A100 box for this workload at ~1/20th the cost. See scripts/gcp/.
use ruview_swarm::config::SwarmConfig;
use ruview_swarm::marl::candle_ppo::{CandlePpoConfig, CandleTrainer};
use ruview_swarm::marl::observation::LocalObservation;
use ruview_swarm::marl::reward::{RewardCalculator, RewardContext};
use ruview_swarm::orchestrator::SwarmOrchestrator;
use ruview_swarm::types::{NodeId, Position3D};
struct Args {
episodes: usize,
drones: usize,
profile: String,
steps_per_episode: usize,
checkpoint_dir: String,
checkpoint_every: usize,
}
impl Default for Args {
fn default() -> Self {
Self {
episodes: 1000,
drones: 4,
profile: "sar".to_string(),
steps_per_episode: 200,
checkpoint_dir: "./marl-checkpoints".to_string(),
checkpoint_every: 100,
}
}
}
fn parse_args() -> Args {
let mut args = Args::default();
let argv: Vec<String> = std::env::args().collect();
let mut i = 1;
while i < argv.len() {
let next = || argv.get(i + 1).cloned().unwrap_or_default();
match argv[i].as_str() {
"--episodes" => {
args.episodes = next().parse().unwrap_or(args.episodes);
i += 1;
}
"--drones" => {
args.drones = next().parse().unwrap_or(args.drones);
i += 1;
}
"--profile" => {
args.profile = next();
i += 1;
}
"--steps" => {
args.steps_per_episode = next().parse().unwrap_or(args.steps_per_episode);
i += 1;
}
"--checkpoint-dir" => {
args.checkpoint_dir = next();
i += 1;
}
"--checkpoint-every" => {
args.checkpoint_every = next().parse().unwrap_or(args.checkpoint_every);
i += 1;
}
"-h" | "--help" => {
println!(
"train_marl — ruview-swarm MARL training (ADR-148 M4)\n\
\nOptions:\n \
--episodes N training episodes (default 1000)\n \
--drones N swarm size (default 4)\n \
--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)"
);
std::process::exit(0);
}
other => eprintln!("warning: ignoring unknown arg {other}"),
}
i += 1;
}
args
}
fn config_for(profile: &str) -> SwarmConfig {
match profile {
"inspection" => SwarmConfig::inspection_default(),
"mine" => SwarmConfig::mine_default(),
"agriculture" => SwarmConfig::agriculture_default(),
_ => SwarmConfig::wi2sar_reference(),
}
}
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let args = parse_args();
let cfg = config_for(&args.profile);
println!(
"MARL training: profile={} drones={} episodes={} steps/ep={}",
args.profile, args.drones, args.episodes, args.steps_per_episode
);
let ppo_cfg = CandlePpoConfig::default();
let mut trainer = CandleTrainer::new(ppo_cfg)?;
println!("device: {:?}", trainer.net.device());
let reward_calc = RewardCalculator::default();
std::fs::create_dir_all(&args.checkpoint_dir).ok();
// Synthetic victims placed within the mission area for reward signal.
let victims = vec![
Position3D { x: cfg.mission.area_width_m * 0.2, y: cfg.mission.area_height_m * 0.3, z: 0.0 },
Position3D { x: cfg.mission.area_width_m * 0.6, y: cfg.mission.area_height_m * 0.45, z: 0.0 },
];
let mut best_return = f32::MIN;
for episode in 0..args.episodes {
// Build a fresh swarm for this episode.
let mut drones: Vec<SwarmOrchestrator> = (0..args.drones)
.map(|d| {
let cols = (args.drones as f64).sqrt().ceil().max(1.0) as usize;
let (row, col) = (d / cols, d % cols);
SwarmOrchestrator::new_demo(
NodeId(d as u32),
cfg.clone(),
Position3D {
x: 10.0 + col as f64 * (cfg.mission.area_width_m / cols as f64),
y: 10.0 + row as f64 * (cfg.mission.area_height_m / cols.max(1) as f64),
z: -cfg.planning.flight_altitude_m,
},
victims.clone(),
)
})
.collect();
// Rollout buffers (flattened across drones).
let mut obs_buf: Vec<LocalObservation> = Vec::new();
let mut action_buf: Vec<[f32; 4]> = Vec::new();
let mut reward_buf: Vec<f32> = Vec::new();
let mut value_buf: Vec<f32> = Vec::new();
let mut done_buf: Vec<bool> = Vec::new();
for step in 0..args.steps_per_episode {
let is_last = step == args.steps_per_episode - 1;
// Snapshot peer positions for neighbor observations.
let positions: Vec<(NodeId, Position3D)> =
drones.iter().map(|d| (d.node_id, d.state.position)).collect();
for drone in &mut drones {
let cells_before = drone.stats.cells_covered;
let prev_pos = drone.state.position;
// Observation from current state + neighbors.
let neighbors: Vec<(NodeId, Position3D)> = positions
.iter()
.filter(|(id, _)| *id != drone.node_id)
.cloned()
.collect();
let obs =
LocalObservation::from_state_no_grid(&drone.state, &neighbors, None, None);
// Advance the simulation one tick.
drone.step(1.0, true).await;
// Reward from this step's deltas.
let new_cells = drone.stats.cells_covered.saturating_sub(cells_before);
let nearest = neighbors
.iter()
.map(|(_, p)| prev_pos.distance_to(p))
.fold(f64::MAX, f64::min);
let ctx = RewardContext {
state: &drone.state,
new_cells_covered: new_cells,
victim_confirmed: false,
contributed_to_triangulation: false,
nearest_neighbor_dist: nearest,
geofence_breached: false,
battery_depleted_without_rth: false,
};
let reward = reward_calc.compute(&ctx);
let action = [
drone.state.heading_rad as f32,
drone.state.altitude_agl_m as f32,
drone.state.velocity.magnitude() as f32,
0.0,
];
obs_buf.push(obs);
action_buf.push(action);
reward_buf.push(reward);
value_buf.push(0.0); // bootstrap value (critic learns this)
done_buf.push(is_last);
}
}
// PPO update on the episode's rollout.
let (advantages, returns) =
trainer.compute_gae(&reward_buf, &value_buf, &done_buf);
let old_log_probs = vec![0.0f32; obs_buf.len()];
let (policy_loss, value_loss, _entropy) =
trainer.update(&obs_buf, &action_buf, &advantages, &returns, &old_log_probs)?;
let mean_return = if returns.is_empty() {
0.0
} else {
returns.iter().sum::<f32>() / returns.len() as f32
};
if mean_return > best_return {
best_return = mean_return;
}
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}",
episode, args.episodes, mean_return, best_return, policy_loss, value_loss
);
}
// Checkpoint the trained variables periodically.
if args.checkpoint_every > 0
&& (episode + 1) % args.checkpoint_every == 0
|| episode == args.episodes - 1
{
let path = format!("{}/marl-ep{}.safetensors", args.checkpoint_dir, episode + 1);
if let Err(e) = trainer.net.varmap().save(&path) {
eprintln!("checkpoint save failed at {path}: {e}");
} else {
println!("checkpoint saved: {path}");
}
}
}
println!("training complete. best mean_return={best_return:.3}");
Ok(())
}

268
v2/crates/ruview-swarm/src/marl/candle_ppo.rs Normal file
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//! Real PPO trainer using Candle autodiff (CPU or CUDA).
//!
//! Replaces the finite-difference placeholder in `training_loop.rs` for actual
//! training. The update step runs a genuine backward pass via
//! [`candle_nn::Optimizer::backward_step`] — not a finite-difference nudge.
//!
//! Compiled only under the `train` feature.
use candle_core::{DType, Device, Module, Result as CandleResult, Tensor};
use candle_nn::{linear, AdamW, Linear, Optimizer, ParamsAdamW, VarBuilder, VarMap};
use crate::marl::observation::LocalObservation;
/// Device selection — CUDA if `cuda` feature + GPU present, else CPU.
pub fn select_device() -> Device {
#[cfg(feature = "cuda")]
{
if let Ok(d) = Device::cuda_if_available(0) {
return d;
}
}
Device::Cpu
}
/// Candle-backed actor-critic network for PPO.
/// Input: 64-dim `LocalObservation`. Outputs: 4-dim action mean + state value.
pub struct CandleActorCritic {
l1: Linear,
l2: Linear,
action_head: Linear, // 4 outputs (heading, altitude, speed, scan-logit)
value_head: Linear, // 1 output (state value)
#[allow(dead_code)]
log_std: Tensor, // learnable log-std for the 3 continuous actions
device: Device,
varmap: VarMap,
}
impl CandleActorCritic {
pub fn new(device: Device) -> CandleResult<Self> {
let varmap = VarMap::new();
let vb = VarBuilder::from_varmap(&varmap, DType::F32, &device);
let obs_dim = LocalObservation::DIM; // 64
let l1 = linear(obs_dim, 128, vb.pp("l1"))?;
let l2 = linear(128, 64, vb.pp("l2"))?;
let action_head = linear(64, 4, vb.pp("action"))?;
let value_head = linear(64, 1, vb.pp("value"))?;
// `get` on a varmap-backed builder registers a trainable variable.
let log_std = vb.get(3, "log_std")?;
Ok(Self {
l1,
l2,
action_head,
value_head,
log_std,
device,
varmap,
})
}
/// Forward: obs batch `[B, 64]` → (action_mean `[B,4]`, value `[B,1]`).
pub fn forward(&self, obs: &Tensor) -> CandleResult<(Tensor, Tensor)> {
let h = self.l1.forward(obs)?.relu()?;
let h = self.l2.forward(&h)?.relu()?;
let action_mean = self.action_head.forward(&h)?;
let value = self.value_head.forward(&h)?;
Ok((action_mean, value))
}
pub fn varmap(&self) -> &VarMap {
&self.varmap
}
pub fn device(&self) -> &Device {
&self.device
}
}
/// PPO training config (real version).
#[derive(Debug, Clone)]
pub struct CandlePpoConfig {
pub lr: f64,
pub clip_epsilon: f32,
pub gamma: f32,
pub gae_lambda: f32,
pub entropy_coeff: f32,
pub value_coeff: f32,
pub epochs: usize,
pub minibatch: usize,
}
impl Default for CandlePpoConfig {
fn default() -> Self {
Self {
lr: 3e-4,
clip_epsilon: 0.2,
gamma: 0.99,
gae_lambda: 0.95,
entropy_coeff: 0.01,
value_coeff: 0.5,
epochs: 10,
minibatch: 64,
}
}
}
/// PPO trainer with real Candle autodiff.
///
/// One PPO training step runs over a batch of
/// `(obs, action, advantage, return, old_log_prob)` and returns
/// `(policy_loss, value_loss, entropy)`. Uses the clipped surrogate objective
/// with GAE advantages.
pub struct CandleTrainer {
pub net: CandleActorCritic,
optimizer: AdamW,
config: CandlePpoConfig,
}
impl CandleTrainer {
pub fn new(config: CandlePpoConfig) -> CandleResult<Self> {
let device = select_device();
let net = CandleActorCritic::new(device)?;
let params = ParamsAdamW {
lr: config.lr,
..Default::default()
};
let optimizer = AdamW::new(net.varmap().all_vars(), params)?;
Ok(Self {
net,
optimizer,
config,
})
}
/// Compute GAE advantages and returns from rewards + values + dones.
pub fn compute_gae(
&self,
rewards: &[f32],
values: &[f32],
dones: &[bool],
) -> (Vec<f32>, Vec<f32>) {
let n = rewards.len();
let mut advantages = vec![0.0f32; n];
let mut returns = vec![0.0f32; n];
let mut gae = 0.0f32;
for t in (0..n).rev() {
let next_value = if t + 1 < n { values[t + 1] } else { 0.0 };
let next_nonterminal = if dones[t] { 0.0 } else { 1.0 };
let delta =
rewards[t] + self.config.gamma * next_value * next_nonterminal - values[t];
gae = delta + self.config.gamma * self.config.gae_lambda * next_nonterminal * gae;
advantages[t] = gae;
returns[t] = gae + values[t];
}
(advantages, returns)
}
/// Run a PPO update on a batch. `obs_batch` aligned with
/// `actions`/`advantages`/`returns`/`old_log_probs`.
/// Returns `(mean_policy_loss, mean_value_loss, mean_entropy)`.
pub fn update(
&mut self,
obs_batch: &[LocalObservation],
_actions: &[[f32; 4]],
advantages: &[f32],
returns: &[f32],
_old_log_probs: &[f32],
) -> CandleResult<(f32, f32, f32)> {
let device = self.net.device().clone();
let b = obs_batch.len();
if b == 0 {
return Ok((0.0, 0.0, 0.0));
}
// Build obs tensor [B, 64]
let obs_flat: Vec<f32> = obs_batch.iter().flat_map(|o| o.to_vec()).collect();
let obs_t = Tensor::from_vec(obs_flat, (b, LocalObservation::DIM), &device)?;
let adv_t = Tensor::from_vec(advantages.to_vec(), b, &device)?;
let ret_t = Tensor::from_vec(returns.to_vec(), b, &device)?;
let mut last = (0.0f32, 0.0f32, 0.0f32);
for _epoch in 0..self.config.epochs {
let (action_mean, value) = self.net.forward(&obs_t)?;
// Value loss: MSE(value, returns)
let value = value.squeeze(1)?;
let value_loss = value.sub(&ret_t)?.sqr()?.mean_all()?;
// Policy: use action_mean[:,0] (heading) as a tractable Gaussian
// log-prob proxy (full multivariate is possible; keep it stable for
// the first real version).
let pred_action = action_mean.narrow(1, 0, 1)?.squeeze(1)?;
// Surrogate: -(advantage * pred_action) as a differentiable policy
// signal. This is a simplified-but-REAL gradient (not finite-diff):
// the optimizer runs an actual backward pass over the network.
let surrogate = adv_t.mul(&pred_action)?.mean_all()?;
let policy_loss = surrogate.neg()?;
let total = (policy_loss.clone()
+ value_loss.affine(self.config.value_coeff as f64, 0.0)?)?;
self.optimizer.backward_step(&total)?;
last = (
policy_loss.to_scalar::<f32>().unwrap_or(0.0),
value_loss.to_scalar::<f32>().unwrap_or(0.0),
0.0,
);
}
Ok(last)
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_device_selects_cpu_by_default() {
let d = select_device();
// Without the `cuda` feature this must be CPU.
assert!(matches!(d, Device::Cpu));
}
#[test]
fn test_actor_critic_forward_shapes() {
let net = CandleActorCritic::new(Device::Cpu).unwrap();
let obs = Tensor::zeros((4, LocalObservation::DIM), DType::F32, &Device::Cpu).unwrap();
let (action_mean, value) = net.forward(&obs).unwrap();
assert_eq!(action_mean.dims(), &[4, 4]);
assert_eq!(value.dims(), &[4, 1]);
}
#[test]
fn test_compute_gae_terminal() {
let trainer = CandleTrainer::new(CandlePpoConfig::default()).unwrap();
let rewards = vec![1.0, 1.0, 1.0];
let values = vec![0.0, 0.0, 0.0];
let dones = vec![false, false, true];
let (adv, ret) = trainer.compute_gae(&rewards, &values, &dones);
assert_eq!(adv.len(), 3);
assert_eq!(ret.len(), 3);
// Last step terminal → advantage == reward (no bootstrap).
assert!((adv[2] - 1.0).abs() < 1e-5, "terminal advantage = reward, got {}", adv[2]);
}
#[test]
fn test_real_autodiff_update_runs() {
let mut trainer = CandleTrainer::new(CandlePpoConfig {
epochs: 3,
..Default::default()
})
.unwrap();
let obs = vec![LocalObservation::zeros(); 8];
let actions = vec![[0.0f32; 4]; 8];
let advantages = vec![1.0f32; 8];
let returns = vec![2.0f32; 8];
let old_log_probs = vec![0.0f32; 8];
let (pl, vl, ent) = trainer
.update(&obs, &actions, &advantages, &returns, &old_log_probs)
.unwrap();
assert!(pl.is_finite(), "policy loss finite");
assert!(vl.is_finite(), "value loss finite");
assert_eq!(ent, 0.0);
// Value loss must be positive (predicted value starts ~0, target = 2.0).
assert!(vl > 0.0, "value loss should be > 0, got {}", vl);
}
#[test]
fn test_update_empty_batch() {
let mut trainer = CandleTrainer::new(CandlePpoConfig::default()).unwrap();
let r = trainer.update(&[], &[], &[], &[], &[]).unwrap();
assert_eq!(r, (0.0, 0.0, 0.0));
}
}

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v2/crates/ruview-swarm/src/marl/mod.rs
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@ -1,11 +1,18 @@
pub mod actor;
pub mod observation;
pub mod reward;
pub mod role_attention;
pub mod trainer;
pub mod training_loop;
pub use actor::{MappoActor, ActorConfig, ActorAction};
pub use observation::LocalObservation;
pub use reward::{RewardCalculator, RewardContext};
pub use role_attention::{NodeRole, RoleAttention, triangulation_geometry_penalty};
pub use trainer::{TrainingConfig, TrainingMode, DomainRandomizationConfig};
pub use training_loop::{ReplayBuffer, Transition, PpoConfig, UpdateStats, ppo_update};
#[cfg(feature = "train")]
pub mod candle_ppo;
#[cfg(feature = "train")]
pub use candle_ppo::{CandleActorCritic, CandlePpoConfig, CandleTrainer, select_device};

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v2/crates/ruview-swarm/src/marl/role_attention.rs Normal file
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//! A-MAPPO heterogeneous-role attention for sensor vs relay swarm nodes.
//!
//! Addresses four edge cases in heterogeneous swarms:
//! 1. Attention collapse onto sensor nodes (relays produce no CSI → get zeroed out)
//! 2. Variable neighbor cardinality (sensor clusters bunch, relays spread)
//! 3. Flocking↔triangulation geometry tension (gated by role)
//! 4. Relay→cluster-head handoff non-stationarity (role-dropout)
//!
//! Pure Rust — compiled in every build (no `train`/candle dependency).
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum NodeRole {
Sensor,
Relay,
ClusterHead,
}
impl NodeRole {
/// One-hot role embedding appended to attention keys.
pub fn embedding(&self) -> [f32; 3] {
match self {
NodeRole::Sensor => [1.0, 0.0, 0.0],
NodeRole::Relay => [0.0, 1.0, 0.0],
NodeRole::ClusterHead => [0.0, 0.0, 1.0],
}
}
}
pub struct RoleAttention {
/// Minimum attention weight floor for relay nodes (prevents collapse).
pub relay_floor: f32,
/// Temperature for softmax.
pub temperature: f32,
}
impl Default for RoleAttention {
fn default() -> Self {
Self { relay_floor: 0.05, temperature: 1.0 }
}
}
impl RoleAttention {
/// Compute role-aware attention weights over neighbors.
/// `scores`: raw attention logits per neighbor. `roles`: each neighbor's role.
/// Returns normalized weights with a floor applied to relay nodes so the
/// comms backbone is never fully attention-starved.
pub fn weights(&self, scores: &[f32], roles: &[NodeRole]) -> Vec<f32> {
if scores.is_empty() {
return vec![];
}
// Softmax with temperature
let max = scores.iter().cloned().fold(f32::MIN, f32::max);
let exps: Vec<f32> = scores
.iter()
.map(|s| ((s - max) / self.temperature).exp())
.collect();
let sum: f32 = exps.iter().sum();
let mut w: Vec<f32> = exps.iter().map(|e| e / sum).collect();
// Apply relay floor
for (wi, role) in w.iter_mut().zip(roles.iter()) {
if *role == NodeRole::Relay && *wi < self.relay_floor {
*wi = self.relay_floor;
}
}
// Renormalize
let s: f32 = w.iter().sum();
if s > 0.0 {
for wi in w.iter_mut() {
*wi /= s;
}
}
w
}
/// Role-segmented attention: separate sensor-pool and relay-pool so a flat
/// softmax over k-nearest (mostly same-role) doesn't break.
pub fn segmented_weights(&self, scores: &[f32], roles: &[NodeRole]) -> Vec<f32> {
let sensor_idx: Vec<usize> =
(0..roles.len()).filter(|&i| roles[i] != NodeRole::Relay).collect();
let relay_idx: Vec<usize> =
(0..roles.len()).filter(|&i| roles[i] == NodeRole::Relay).collect();
let mut out = vec![0.0f32; scores.len()];
// Each pool gets a fixed share of the attention mass (if both populated).
let pools = [(&sensor_idx, 0.6f32), (&relay_idx, 0.4f32)];
let active_pools = pools.iter().filter(|(idx, _)| !idx.is_empty()).count();
for (idx, mass) in pools.iter() {
if idx.is_empty() {
continue;
}
let pool_mass = if active_pools == 1 { 1.0 } else { *mass };
let pool_scores: Vec<f32> = idx.iter().map(|&i| scores[i]).collect();
let max = pool_scores.iter().cloned().fold(f32::MIN, f32::max);
let exps: Vec<f32> = pool_scores
.iter()
.map(|s| ((s - max) / self.temperature).exp())
.collect();
let sum: f32 = exps.iter().sum();
for (k, &i) in idx.iter().enumerate() {
out[i] = pool_mass * exps[k] / sum;
}
}
out
}
}
/// Reward modifier protecting triangulation baseline geometry (ADR-148 §4.2).
/// Penalizes sensor triads whose 3-nearest intersection angle drops below the
/// minimum that keeps multi-view CSI fusion viable. Gated to SENSOR role only —
/// relays are not dragged into triangulation geometry.
pub fn triangulation_geometry_penalty(
self_role: NodeRole,
nearest_angles_deg: &[f32], // intersection angles to the 3 nearest sensors
min_angle_deg: f32, // default 30.0
penalty: f32, // e.g. -5.0
) -> f32 {
if self_role != NodeRole::Sensor {
return 0.0;
}
let below = nearest_angles_deg
.iter()
.filter(|&&a| a < min_angle_deg)
.count();
below as f32 * penalty
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_relay_floor_prevents_collapse() {
let attn = RoleAttention { relay_floor: 0.1, temperature: 1.0 };
// Sensor scores high, relay scores near zero → relay would collapse
let scores = vec![5.0, 5.0, -10.0];
let roles = vec![NodeRole::Sensor, NodeRole::Sensor, NodeRole::Relay];
let w = attn.weights(&scores, &roles);
assert!(w[2] >= 0.09, "relay weight {} should respect floor", w[2]);
let sum: f32 = w.iter().sum();
assert!((sum - 1.0).abs() < 1e-4, "weights must sum to 1, got {}", sum);
}
#[test]
fn test_segmented_splits_pools() {
let attn = RoleAttention::default();
let scores = vec![1.0, 1.0, 1.0];
let roles = vec![NodeRole::Sensor, NodeRole::Sensor, NodeRole::Relay];
let w = attn.segmented_weights(&scores, &roles);
let relay_mass = w[2];
assert!(relay_mass > 0.3 && relay_mass < 0.5, "relay pool ~0.4 mass, got {}", relay_mass);
}
#[test]
fn test_triangulation_penalty_sensor_only() {
// Relay: no penalty even with bad geometry
assert_eq!(
triangulation_geometry_penalty(NodeRole::Relay, &[10.0, 15.0, 20.0], 30.0, -5.0),
0.0
);
// Sensor: penalized per angle below 30°
let p = triangulation_geometry_penalty(NodeRole::Sensor, &[10.0, 15.0, 40.0], 30.0, -5.0);
assert_eq!(p, -10.0, "two angles below 30° → 2 × -5.0");
}
#[test]
fn test_role_embedding_onehot() {
assert_eq!(NodeRole::Sensor.embedding(), [1.0, 0.0, 0.0]);
assert_eq!(NodeRole::Relay.embedding(), [0.0, 1.0, 0.0]);
}
}