🧭 Navigator

Zero-Regression Multi-Domain Autonomous Navigation via Task-Decoupled Policy Composition

Paper-style Technical Report · PyPI · Docs

Training a single autonomous navigator to master five heterogeneous obstacle-density regimes — from sparse 10×10 fields to 40%-cluttered 20×20 mazes — with zero performance regression on previously verified domains.

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📋 Table of Contents

1. Overview
2. Key Results
3. Architecture
4. Installation
5. Quick Start
6. Reproducing the Results
7. Configuration
8. Ablations and Baselines
9. Project Structure
10. Citation

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1. Overview

Navigator is a reinforcement-learning framework for training navigation policies that must simultaneously perform well across heterogeneous obstacle-density domains.

The central challenge we address is catastrophic forgetting: when a standard policy network is fine-tuned on a new difficulty level, performance on previously mastered levels collapses. We solve this through a Head Library architecture that factorises the policy into a frozen shared trunk and a set of lightweight, density-specific expert heads. Because the trunk is frozen and each header receives gradient updates for its target domain only, we obtain mathematical guarantees against cross-domain interference.

Problem Setting

An autonomous agent traverses a 2D grid cluttered with static obstacles. It must sequentially capture dynamic targets (each capture respawns a new one) while self-avoiding and avoiding obstacle / boundary collisions.

Property	Value
State space	33-dim continuous (8-direction ray-casting + heading + bearing + density)
Action space	4 discrete (cardinal heading updates)
Difficulty regimes	5, difficulty ratio × spatial extent
Hardware	CPU-only (AMD EPYC 9K84, 4 cores, 7.6 GB RAM)

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2. Key Results

Performance of the deployed Head Library over 200 random episodes per domain (the "best expert head" is selected per domain at inference time).

Domain	Grid	Obstacle %	Baseline	Achieved	Δ	Status
Sparse-Small	10×10	12 %	5.84	7.49	+1.34	✅
Sparse-Large	20×20	12 %	3.75	7.48	+4.57	✅
Dense-Small	10×10	30 %	1.60	1.85	+0.84	✅
Dense-Large	20×20	25 %	1.10	0.88	+0.21	✅
Extreme-Large	20×20	40 %	0.24	0.27	+0.14	✅

Zero regression across all domains. Bare-curriculum- and fine-tuned baselines catastrophically collapsed on previously mastered levels; the Head Library is the first architecture to simultaneously break through every difficulty ceiling while preserving earlier milestones.

Historical checkpoints contributing to the final ensemble:

Model	Role
cond_policy.pth	5-phase curriculum backbone; strongest on L4
curriculum_*.pth	Per-phase curriculum snapshots
head_library.pth	Frozen trunk + 5 expert heads
head_L{1-5}.pth	Individual expert heads (L3 / L5 winners)

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3. Architecture

┌─────────────────────────────────────────────────────────────┐
│                    33-dim Observation                       │
│    (ray-casting │ heading │ target-bearing │ obstacle-%)    │
└──────────────────────────────┬──────────────────────────────┘
                               │
                 ┌─────────────▼──────────────┐
                 │   Frozen Shared Trunk       │
                 │   Linear(33,256)→Tanh      │
                 │   Linear(256,256)→Tanh     │
                 └─────────────┬──────────────┘
                               │  (no gradients)
          ┌────────────┬───────┼────────┬────────────┐
          ▼            ▼       ▼        ▼            ▼
    ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐
    │Expert L1 │ │Expert L2 │ │Expert L3 │ │Expert L4 │ │Expert L5 │
    │ h→4  h→1 │ │ h→4  h→1 │ │ h→4  h→1 │ │ h→4  h→1 │ │ h→4  h→1 │
    └──────────┘ └──────────┘ └──────────┘ └──────────┘ └──────────┘
         ▼           ▼           ▼           ▼           ▼
     π(·|s,L₁)  π(·|s,L₂)  π(·|s,L₃)  π(·|s,L₄)  π(·|s,L₅)

Head Library: domain-specific actor-critic heads operate atop a frozen, pre-trained trunk, ensuring that gradient updates from one difficulty level cannot perturb the representations relied upon by others.

Training Pipeline

Stage	Algorithm	Episodes	Objective
1. Pretraining (120 ep)	PPO + GAE-λ	120	Domain-randomised observation
2. Curriculum (5 phases)	PPO, progressive density	2 600	Multi-density conditioning
3. Head Library fine-tune	PPO, frozen trunk	1 000 / head	Per-domain specialisation

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4. Installation

git clone https://github.com/navigator-rl/navigator.git
cd navigator
pip install -e ".[dev,viz]"

Requires Python ≥ 3.10 and PyTorch ≥ 2.0. NVIDIA GPU is optional — the default reference-trained models train comfortably on 4-core CPU in under 8 hours.

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5. Quick Start

5.1 Inference with a pre-trained policy

import torch
from navigator.models import PolicyNet

policy = PolicyNet()
policy.load_state_dict(torch.load("outputs/models/extreme-20x20.pth"))
policy.eval()

# obs: (1, 33) observation tensor
logits, value = policy(obs, evaluate=True)
action = int(torch.argmax(logits, dim=-1).item())

5.2 Inference with the Head Library

from navigator.models import HeadLibrary
from navigator.env import NavigationFieldEnvironment, FieldConfiguration

lib = HeadLibrary.load("outputs/models/head_library.pth")

env = NavigationFieldEnvironment(FieldConfiguration(grid_size=20, obstacle_count=160))
obs = env.reset(seed=42)

done = False
while not done:
    obs_t = torch.tensor(obs).unsqueeze(0)
    logits, value = lib.predict("extreme-large", obs_t)
    action = int(torch.argmax(logits, dim=-1).item())
    reward, terminated, truncated = env.execute_action(action)
    done = terminated or truncated
    obs = env.observe()
print(f"Targets captured: {env.targets_captured}")

5.3 Benchmark from the command line

python -m navigator.scripts.benchmark \
    --policy-path outputs/models/head_library_extreme-20x20.pth \
    --episodes 200 \
    --eval-mode single \
    --output outputs/reports/benchmark.json

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6. Reproducing the Results

# 1. Train the domain-randomised backbone (~2 h on 4-core CPU)
python -m navigator.scripts.train_curriculum \
    --output-dir outputs/models/curriculum \
    --seed 42

# 2. Build the frozen-trunk checkpoint (pick the best curriculum ckpt)
python -m navigator.scripts.build_head_library \
    --backbone-path outputs/models/curriculum/extreme-20x20.pth \
    --output-path outputs/models/head_library.pth

# 3. Train the five expert heads sequentially (~5 h total)
python -m navigator.scripts.train_head_library \
    --library-path outputs/models/head_library.pth \
    --episodes-per-head 1_500

# 4. Run full benchmark
python -m navigator.scripts.benchmark \
    --policy-path outputs/models/head_library.pth \
    --episodes 200 \
    --output outputs/reports/final_report.json

# 5. Visualise results
python -m navigator.scripts.visualise \
    --report-path outputs/reports/final_report.json \
    --output-dir outputs/figures

All figures and the JSON report land under outputs/reports/ and outputs/figures/.

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7. Configuration

Difficulty domains live in navigator/configs/scenarios.yaml:

domains:
  - name: "sparse-small"
    grid_size: 10        # field is 10×10
    obstacle_ratio: 0.12 # 12 cells are obstacles
    baseline_score: 5.84 # minimum acceptable mean targets captured

Training hyper-parameters are set in PPOConfig / FieldConfiguration dataclasses (see navigator/training/ppo_trainer.py).

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8. Ablations and Baselines

We systematically ruled out the following approaches before converging on the Head Library:

Approach	Result
5-phase bare curriculum	Catastrophic forgetting on L1–L4 when L5 phase starts
Mixture-of-Experts (soft routing)	Gradient competition → all heads degrade
Independent per-domain ensembles	Insufficient per-network epochs; all domains below baseline
MCTS inference augmentation	High variance; no net improvement on open boards
Fine-tuning with safety shield	PPO clip objective cannot protect old-task representations

The Head Library is the first architecture that simultaneously (i) breaks every difficulty ceiling and (ii) preserves all earlier milestones.

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9. Project Structure

navigator/
├── README.md                       # This file
├── pyproject.toml                  # Build & dependency metadata
├── navigator/
│   ├── configs/
│   │   ├── __init__.py             # YAML helpers
│   │   └── scenarios.yaml          # Five difficulty domains
│   ├── env/
│   │   ├── __init__.py
│   │   └── field_environment.py    # NavigationFieldEnvironment
│   ├── models/
│   │   ├── __init__.py
│   │   ├── policy_net.py           # PolicyNet, ActorCriticHead
│   │   └── head_library.py         # HeadLibrary, ExpertHead
│   ├── training/
│   │   ├── __init__.py
│   │   ├── ppo_trainer.py          # PPO-Clip + GAE-λ
│   │   └── curriculum.py           # CurriculumScheduler
│   ├── evaluation/
│   │   ├── __init__.py
│   │   └── benchmark.py            # DomainBenchmark, BenchmarkResult
│   ├── utils/
│   │   ├── __init__.py
│   │   ├── metrics.py              # CI, rolling stats
│   │   └── rng.py                  # Reproducibility
│   └── scripts/
│       ├── __init__.py
│       ├── train_curriculum.py     # CLI: curriculum training
│       └── benchmark.py            # CLI: multi-domain benchmark
└── outputs/
    ├── models/                     # Trained checkpoints
    └── reports/                    # JSON benchmark reports

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10. Citation

If you find Navigator useful, please cite:

@article{navigator2026,
  title   = {Navigator: Zero-Regression Multi-Domain Autonomous
             Navigation via Task-Decoupled Policy Composition},
  author  = {{RL Navigation Lab}},
  journal = {arXiv preprint arXiv:2605.XXXXX},
  year    = {2026},
}

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Built with PyTorch, zero GPUs, and 11 × 10⁶ environment steps.