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How to preview a brain's responses without altering live memory with Cadence

Use imagination to inspect possible responses while the live trace and pending feedback stay unchanged.

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

Who it is forA developer debugging a Cadence brain who needs to see how it would respond to a sequence of observations without disturbing its ongoing experience.

The problemProbing a live learning agent often changes its memory or random state, which makes the probe itself corrupt the experiment.

The outcomeA list of imagined phases that can be inspected, with live memory, random state and pending feedback left as they were.

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. Compose a brain with brain = Brain.compose(inputs=4, actions=2, arousal=True) and run a few live calls to build some experience.
  3. Pass a list of observation arrays to brain.imagine([observation, next_observation]).
  4. Inspect each returned phase, reading phase.qualified before using any imagined response.
  5. Continue the live loop and confirm that the next live action is consistent with a run without the imagine call.

Prompt for your agent

Using the Python package cadence-net (repo muellerberndt/cadence), compose a brain with inputs=4 and actions=2, run live() for 50 steps, then call imagine() on <observation sequence> and print each phase's qualified flag. Expected output: the qualified flags and the live action taken afterward. Verify that the live action sequence matches a control run that skips imagine().

Caveat. The README says imagined responses must be checked for qualification before use, so treat unqualified phases as unreliable.

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