Add: Phase 6 - Testing, Optimization, and Deployment Complete

[lalomorales22]

This commit completes Phase 6 of the FlyingCarRL project, adding comprehensive testing infrastructure, performance optimization tools, compliance features, CI/CD automation, and cloud deployment capabilities.

Task 6.1: Unit and Integration Tests ✅

• Add pytest configuration (pytest.ini) with test markers and coverage settings
• Create comprehensive test suite with 50+ test cases:
  • tests/conftest.py - Shared fixtures and test utilities
  • tests/test_database.py - Database functionality tests
  • tests/test_environment.py - Environment and physics tests
  • tests/test_training.py - RL training pipeline tests
  • tests/test_spawning.py - Vehicle spawning system tests
  • tests/test_integration.py - End-to-end integration tests
  • tests/test_compliance.py - Compliance and safety feature tests
• Test coverage target: 70% minimum
• Support for unit, integration, slow, GPU, physics, database, and convergence test markers

Task 6.2: Performance Optimization ✅

• Add profiling utilities (src/utils/profiling.py):
  • PerformanceProfiler for function-level timing
  • GPUProfiler for GPU memory tracking
  • ThroughputMonitor for steps/episodes per second
• Add profiling script (scripts/profile_training.py) for comprehensive training profiling
• Add distributed training support (scripts/distributed_training.py) with Ray
• Enable multi-GPU and multi-node training capabilities

Task 6.3: Compliance Features ✅

• Implement aviation compliance module (src/utils/compliance.py):
  • NoFlyZone class for restricted areas
  • GeofenceCorridor class for approved flight paths
  • ComplianceManager for multi-zone management
  • ComplianceRewardWrapper for RL integration
• Support altitude limits, speed limits, separation requirements
• Violation tracking and penalty calculation
• Statistics and reporting capabilities

Task 6.4: Documentation and CI/CD ✅

• Add GitHub Actions workflows:
  • .github/workflows/tests.yml - Automated testing across Python 3.10-3.12
  • .github/workflows/code-quality.yml - Code formatting and linting
  • .github/workflows/docker-build.yml - Docker image building
• Create comprehensive Phase 6 documentation (docs/PHASE_6_SUMMARY.md)
• Update README with Phase 6 completion status
• Expand Testing and Evaluation section with new capabilities

Task 6.5: Cloud Deployment Options ✅

• Add Docker support:
  • Dockerfile with multi-stage builds for optimized images
  • docker-compose.yml for multi-service deployment
  • .dockerignore for efficient builds
• Add AWS deployment script (deploy/aws/deploy_ec2.sh)
• Add GCP deployment script (deploy/gcp/deploy_gce.sh)
• Create comprehensive deployment documentation (deploy/README.md)
• Support for MySQL database, Ray cluster, and Streamlit dashboard

Files Added/Modified

New files:

• Testing: pytest.ini, tests/*
• Profiling: src/utils/profiling.py, scripts/profile_training.py
• Distributed: scripts/distributed_training.py
• Compliance: src/utils/compliance.py
• CI/CD: .github/workflows/*
• Deployment: Dockerfile, docker-compose.yml, deploy/*
• Documentation: docs/PHASE_6_SUMMARY.md

Modified files:

• README.md: Mark Phase 6 as complete, expand Testing section

Milestone Achieved

✅ Phase 6 complete - Production-ready deployment with testing, optimization,
compliance features, CI/CD automation, and cloud deployment options
✅ Release v1.0 ready with end-to-end training capabilities

The platform is now ready for large-scale training experiments, multi-agent coordination research, and production deployment.