More realistic random domain generation required

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
25/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Stale
Tech stack
python

Research direction

Start by reading cyberbattle.simulation.generate_network and cyberbattle.simulation.environment_generation, then run the existing random environments to observe node discovery and hackability. Define measurable criteria for a more complex, dynamic, realistic environment that supports agent generalization; completion requires those generators to produce deeper, more consistently usable networks.

Written by the indexing model from the issue text.

Description

enhancement

The static toy environments are not suitable for training an agent for generalization. The chainpattern is solvable with a trivial strategy, which is not enough to demonstrate that a certain method is good enough.

The random environment in cyberbattle.simulation.generate_network is not realistic and deep enough. In a random network of 50 nodes, only 1 or 2 are actually hackable.

The random environment in cyberbattle.simulation.environment_generation seem to be very limited as well (usually, no other nodes discovered).

To train generalizing agents, a more complex, dynamic and realistic environment generation is needed.

Dominant language
Jupyter Notebook
Stars
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Forks
286
Avg merge
4h 35m
Merged PRs (30d)
12

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