research1d
Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents
Researchers propose a method to learn red agent policy from observations for neurosymbolic autonomous cyber agents. The approach uses reinforcement learning and behavior trees with learning-enabled components. This method aims to improve autonomous cyber-defense in partially observable systems. You can apply this approach to develop more adaptive security systems.
Key takeaways
- Uses reinforcement learning and behavior trees with learning-enabled components.
- Aims to improve autonomous cyber-defense in partially observable systems.
- Method learns red agent policy from observations.