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MARL Development Updates

This page tracks the development progress of the OpenCDA-MARL extension, documenting all modifications, features, and improvements across versions.

Version Tracking

Current stable release: v1.0.0 — Complete MARL framework with five RL algorithms, training infrastructure, and evaluation tools.

Release Timeline

gantt
    title MARL Development Timeline
    dateFormat  YYYY-MM-DD
    section Framework
    Foundation & Adapters            :done, 2025-08-07, 35d
    RL Algorithms & Training         :done, 2025-09-15, 75d
    Stabilization & Documentation    :done, 2025-12-01, 60d
    section Releases
    v1.0.0 Stable Release            :milestone, 2026-01-30, 0d
    section Planned
    v1.1.0 (Advanced Features)       :2026-02-01, 60d
Version Release Date Status Highlights
1.0.0 2026-01-30 Stable Complete MARL framework, 5 algorithms, training, GUI
1.1.0 2026 Q1 Planned Advanced features & optimizations

v1.0.0 (Stable Release)

No Breaking Changes

The MARL extension is designed to be non-invasive. All OpenCDA functionality remains unchanged.

The first stable release consolidates all development phases into a complete, production-ready MARL framework.

Core Framework

OpenCDA-MARL provides a comprehensive 3-layer architecture for multi-agent reinforcement learning in autonomous driving. The system includes four agent types (behavior, vanilla, rule-based, MARL) managed through a centralized factory pattern. Vehicle adapters bridge OpenCDA's autonomous driving stack with MARL control systems.

The MARLEnv environment provides custom CARLA integration with observation extraction, multi-objective reward calculation, and cross-agent evaluation. A PySide6 Qt-based GUI dashboard enables real-time visualization and interactive control.

RL Algorithm Suite

Five RL algorithms with a shared BaseAlgorithm interface:

Algorithm Type Key Features
TD3 Continuous LSTM encoder, multi-agent context, prioritized replay
DQN Discrete Epsilon-greedy, target network, gradient clipping
Q-Learning Discrete Tabular, configurable state bins
MAPPO On-Policy GAE, rollout buffer, Gaussian actor
SAC Continuous Entropy regularization, auto-tuning alpha
  • MARLManager: Algorithm orchestrator with training/evaluation modes
  • ObservationExtractor: 9 configurable feature types for RL observation vectors
  • CheckpointManager: Structured model saving (latest, best, per-episode)
  • TrainingMetrics: Per-episode statistics with CSV export
  • SmartReplayBuffer: Pre-allocated numpy arrays with recency bias (50% recent + 50% diverse)
  • PrioritizedReplayBuffer: TD-error weighted experience sampling
  • TensorBoard: Comprehensive logging (losses, Q-values, gradients, convergence)
  • Convergence Detection: CV-based (< 15%), success rate stability, min 20 episodes
  • SumoMarlEnv: Lightweight SUMO-only environment for traffic simulation
  • SUMO adapter/spawner: Vehicle spawning and control in SUMO
  • Enables large-scale experiments without CARLA overhead

Planned: v1.1.0

  • Advanced multi-agent communication protocols
  • Curriculum learning for progressive scenario difficulty
  • Distributed training support
  • Highway and parking scenario templates
  • Performance benchmarking across algorithms

Changelog Template

When adding changelog entries:

  1. Version File: Create/update version-specific file (e.g., v1.1.0.md)
  2. Categories: Use consistent categories (Architecture, APIs, Config, etc.)
  3. Impact: Note breaking changes and migration requirements
  4. Examples: Include code examples for significant changes
  5. Links: Reference related issues, PRs, and documentation

Version Scheme

MARL follows semantic versioning: MAJOR.MINOR.PATCH

  • MAJOR: Breaking changes
  • MINOR: New features (backward compatible)
  • PATCH: Bug fixes, documentation updates
# Version X.Y.Z Changelog

**Release Date**: YYYY-MM
**Status**: Current/Development/Planned
**Theme**: Brief description

## Major Features
[Feature descriptions with code examples]

## Technical Details
[Implementation details]

## Bug Fixes
[Fixed issues]

## Performance
[Performance improvements]

## Known Limitations
[Current limitations]

## API Changes
[New/modified APIs]

## Migration Notes
[Migration instructions]

Stay Updated

Watch the GitHub repository for the latest updates and releases.