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Documentation Index

Fetch the complete documentation index at: https://mintlify.com/SeanCole02/doom-neuron/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

For local development and testing, you can run both the CL1 neural interface and training server on the same machine. This setup is ideal for:
  • Testing code changes without hardware
  • Debugging training pipelines
  • Developing with the SDK
  • Validating configurations before remote deployment
Local mode uses localhost (127.0.0.1) for all network communication. The CL1 neural interface must be started before the training server.

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Quick Start

1

Start CL1 Neural Interface

Run the CL1 interface first using the convenience script:
./scripts/run_sdk_cl1.sh
This executes:
python cl1_neural_interface.py \
    --training-host 127.0.0.1 \
    --recording-path recordings \
    --tick-frequency 10
What this does:
  • Connects to training server at 127.0.0.1 (localhost)
  • Saves recordings to ./recordings directory
  • Runs neural loop at 10 Hz to avoid overstimulation
2

Start Training Server

After the CL1 interface is running, launch the training server:
./scripts/run_sdk_training_server.sh
This executes:
python training_server.py \
    --mode train \
    --device cpu \
    --cl1-host 127.0.0.1 \
    --max-episodes 1000
What this does:
  • Runs in training mode with PPO reinforcement learning
  • Uses CPU device (change to cuda if GPU available)
  • Connects to CL1 interface at 127.0.0.1
  • Trains for up to 1000 episodes
3

Monitor Training (Optional)

Open visualisation.html in a web browser. Update the image source to point to your training server:
<img id="img" width="640" src="http://127.0.0.1:12349/doom.mjpeg">
This provides a real-time MJPEG stream of the DOOM game state.

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Manual Setup

If you need custom configuration, run the commands manually: 
python cl1_neural_interface.py --training-host localhost

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Common Options

ArgumentDefaultDescription
--training-hostrequiredIP of training system (use localhost or 127.0.0.1)
--tick-frequency10Neural loop frequency in Hz
--recording-path./recordingsDirectory for neural recordings
--stim-port12345Port for receiving stimulation commands
--spike-port12346Port for sending spike data
--event-port12347Port for receiving event metadata
--feedback-port12348Port for receiving feedback commands

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Example with Custom Recording Path

python cl1_neural_interface.py \
    --training-host localhost \
    --recording-path /data/local_test_recordings \
    --tick-frequency 10

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Network Ports

Local setup uses the following UDP ports on 127.0.0.1:
  • 12345 - Stimulation commands (training → CL1)
  • 12346 - Spike data (CL1 → training)
  • 12347 - Event metadata (training → CL1)
  • 12348 - Feedback commands (training → CL1)
  • 12349 - MJPEG stream (training → browser)
These ports must be available. Check with netstat -an | grep 1234 before starting.

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Output Files

After training, you’ll find: Training Server outputs: 
checkpoints/
├── episode_*.pt          # Model checkpoints
└── l5_2048_rand/
    └── logs/             # TensorBoard logs

training_log.jsonl        # Episode statistics
CL1 Interface outputs: 
recordings/
└── *.cl1                 # Neural recordings with metadata

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Stopping Training

Press Ctrl+C in either terminal to gracefully shutdown:
  1. Training server sends completion signal
  2. CL1 interface saves recording and exits
  3. Both processes cleanup UDP sockets
Always stop gracefully with Ctrl+C to ensure recordings and checkpoints are saved properly.

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Next Steps

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