| Name | Version | Summary | date |
| dm-meltingpot |
2.4.0 |
A suite of test scenarios for multi-agent reinforcement learning. |
2024-12-19 12:40:33 |
| csle-attack-profiler |
0.7.1 |
Library with MITRE attack profiler for CSLE |
2024-12-13 12:02:48 |
| gym-csle-cyborg |
0.7.1 |
OpenAI gym reinforcement learning environment wrapper for CybORG |
2024-12-13 12:02:42 |
| gym-csle-apt-game |
0.7.1 |
OpenAI gym reinforcement learning environment of a Dynkin (Optimal stopping) APT game in CSLE |
2024-12-13 12:02:40 |
| csle-tolerance |
0.7.1 |
An intrusion-tolerant system: Tolerance: (T)w(o)-(l)ev(e)l (r)ecovery (a)nd respo(n)se (c)ontrol with f(e)edback. |
2024-12-13 12:02:37 |
| csle-cluster |
0.7.1 |
Scripts for cluster management in CSLE |
2024-12-13 12:02:33 |
| csle-cli |
0.7.1 |
CLI tool for CSLE |
2024-12-13 12:02:30 |
| csle-rest-api |
0.7.1 |
CSLE REST API |
2024-12-13 12:02:27 |
| csle-agents |
0.7.1 |
Reinforcement learning agents for CSLE |
2024-12-13 12:02:22 |
| gym-csle-intrusion-response-game |
0.7.1 |
OpenAI gym reinforcement learning environment of an intrusion response game in CSLE |
2024-12-13 12:02:19 |
| gym-csle-stopping-game |
0.7.1 |
OpenAI gym reinforcement learning environment of a Dynkin (Optimal stopping) game in CSLE |
2024-12-13 12:02:15 |
| csle-system-identification |
0.7.1 |
Scripts for system identification in CSLE |
2024-12-13 12:02:09 |
| csle-defender |
0.7.1 |
Scripts for emulated defenses in CSLE |
2024-12-13 12:02:05 |
| csle-attacker |
0.7.1 |
Scripts for emulated cyber attacks in CSLE |
2024-12-13 12:02:01 |
| csle-common |
0.7.1 |
Common functionality of the Cyber Security Learning Environment (CSLE) |
2024-12-13 12:01:56 |
| csle-collector |
0.7.1 |
Scripts for data collection in CSLE |
2024-12-13 12:01:50 |
| csle-ryu |
0.7.1 |
RYU SDN Controllers in CSLE |
2024-12-13 12:01:44 |
| csle-base |
0.7.1 |
Library with base classes and definitions for CSLE |
2024-12-13 12:00:02 |
| sinergym |
3.7.0 |
Sinergym provides a Gymnasium-based interface to interact with building simulations. This allows control in simulation time through custom controllers, including reinforcement learning agents |
2024-12-11 11:58:09 |
| pcgym |
0.1.8 |
Reinforcement learning suite of process control problems. |
2024-12-03 13:42:08 |