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How to run a continuing brain that learns from live rewards with Cadence

Compose a System 1 brain and feed it observations and measured rewards in one ongoing stream.

  • Top pick
  • Research
  • Intermediate
  • 30 minutes
  • From the README
  • Any agent

Who it is forA researcher or developer testing an agent that must keep learning as its environment changes, with no separate training and inference phases.

The problemMost learning setups split training from use, so an agent cannot keep adapting to a stream of outcomes in one continuing run.

The outcomeA running brain that returns one action per observation, takes the previous action's reward on each call, and reports its settlement work.

What you need first

  • Python 3.11 or newer
  • NumPy installed through pip
  • A toy environment or task that can supply an observation and a measured reward after each action

Steps

  1. python -m pip install cadence-net==0.84.3
  2. Create a Python file that runs brain = Brain.compose(inputs=4, actions=2, arousal=True) from cadence.
  3. Call action = brain.live(observation) with one observation row, execute the action in your environment, and measure the reward.
  4. Call action = brain.live(next_observation, reward=reward) for each following step, passing the reward of the preceding action.
  5. Read brain.last_settlement after each call to check residual, qualification and sweeps.

Prompt for your agent

Using the Python package cadence-net (repo muellerberndt/cadence), write a loop that drives Brain.compose(inputs=4, actions=2, arousal=True) through live() calls on the toy environment at <environment file>. Expected output: a printed log of actions, rewards and last_settlement values for 200 steps. Verify that each call after the first passes the reward of the previous action.

Caveat. Cadence is alpha research software, so the first run tests the library's mechanisms rather than delivering a production controller.

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