docker-compose.yml

version: '3.8'

services:
  rl-bridge:
    build:
      context: .
      dockerfile: docker/Dockerfile
    image: v2x-qos-bridge:latest
    container_name: v2x-rl-bridge
    command: ["/workspace/tools/rl_bridge/venv/bin/python3", "/workspace/tools/rl_bridge/scripts/train_online.py"]
    ports:
      - "8080:8080"
    volumes:
      # Mount the project root to synchronize code edits in real-time
      - .:/workspace
      # Protect and preserve the container's built-in virtualenv and third-party libraries
      - /workspace/tools/rl_bridge/venv
      - /workspace/third_party
    stdin_open: true
    tty: true

  qos-simulation:
    build:
      context: .
      dockerfile: docker/Dockerfile
    image: v2x-qos-bridge:latest
    container_name: v2x-qos-simulation
    # Use network_mode: "service:rl-bridge" so the C++ simulation shares the exact localhost
    # network namespace as the python server. This enables communication on 127.0.0.1:8080.
    network_mode: "service:rl-bridge"
    # Delay startup slightly to guarantee that the Python server is listening on port 8080
    command: >
      bash -c "sleep 3 && ./run_experiments.sh unpatched --train-rl"
    environment:
      # Configure the dynamic library loader to find the ONNX Runtime library inside the container
      - LD_LIBRARY_PATH=/workspace/third_party/onnxruntime/lib
    volumes:
      # Mount project root and preserve container libraries identically to the bridge
      - .:/workspace
      - /workspace/tools/rl_bridge/venv
      - /workspace/third_party
      # Protect and preserve the container-built binaries to prevent host WSL builds from masking them
      - /workspace/vanetza_unpatched/build
      - /workspace/vanetza_patched/build
    depends_on:
      - rl-bridge