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
- Domain
- machine-learning, networking
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
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
- 1.8k
- Forks
- 286
- Avg merge
- 4h 35m
- Merged PRs (30d)
- 12
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