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3110 - docs formatting fixes and updating text

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Marek Wolan
2025-03-13 11:29:03 +00:00
parent 8b91c280cb
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docs/source/glossary.rst
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@@ -9,10 +9,10 @@ Glossary
:sorted:
Network
The network in primaite is a logical representation of a computer network containing :term:`Nodes<Node>` and :term:`Links<Link>`.
The network in primaite is a logical representation of a computer network containing :term:`Nodes<Node>` and :term:`Links<Link>`. See :ref:`network`.
Node
A Node represents a network endpoint. For example a computer, server, switch, or an actuator.
A Node represents a network endpoint. For example a computer, server, switch, or an actuator. See :ref:`node_description`
Link
A Link represents the connection between two Nodes. For example, a physical wire between a computer and a switch or a wireless connection.
@@ -21,7 +21,7 @@ Glossary
Protocols are used by links to separate different types of network traffic. Common examples would be HTTP, TCP, and UDP.
Service
A service represents a piece of software that is installed on a node, such as a web server or a database.
A service represents a piece of software that is installed on a node, such as a web server or a database. See :ref:`software`
Access Control List
PrimAITE blocks or allows certain traffic on the network by simulating firewall rules, which are defined in the Access Control List.
@@ -42,7 +42,7 @@ Glossary
PoLs allow agents to change the current hardware, OS, file system, or service statuses of nodes during the course of an episode. For example, a green agent may restart a server node to represent scheduled maintainance. A red agent's Pattern-of-Life can be used to attack nodes by changing their states to CORRUPTED or COMPROMISED.
Reward
The reward is a single number used by the blue agent to understand whether it's performing well or poorly. RL agents change their behaviour in an attempt to increase the expected reward each episode. The reward is generated based on the current states of the environment and is impacted positively by things like green PoL running successfully and negatively by things like nodes being compromised.
The reward is a single number used by the blue agent to understand whether it's performing well or poorly. RL agents change their behaviour in an attempt to increase the expected reward each episode. The reward is generated based on the current states of the environment and is impacted positively by things like green PoL running successfully and negatively by things like nodes being compromised. See :ref:`Rewards`
Observation
An observation is a representation of the current state of the environment that is given to the learning agent so it can decide on which action to perform. If the environment is 'fully observable', the observation contains information about every possible aspect of the environment. More commonly, the environment is 'partially observable' which means the learning agent has to make decisions without knowing every detail of the current environment state.
@@ -50,6 +50,9 @@ Glossary
Action
The learning agent decides on an action to take on every step in the simulation. The action has the chance to positively or negatively impact the environment state. Over time, the agent aims to learn which actions to take when to maximise the expected reward.
Action mask
An input to RL algorithms that contains information about which of the actions in the action space are currently valid. See :ref:`action_masking`
Training
During training, an RL agent is placed in the simulated network and it learns which actions to take in which scenarios to obtain maximum reward.
@@ -69,4 +72,13 @@ Glossary
PrimAITE uses the Gymnasium reinforcement learning framework API to create a training environment and interface with RL agents. Gymnasium defines a common way of creating observations, actions, and rewards.
User app home
PrimAITE supports upgrading software version while retaining user data. The user data directory is where configs, notebooks, and results are stored, this location is `~/primaite<version>/` on linux/darwin and `C:\\Users\\<username>\\primaite<version>` on Windows.
PrimAITE supports upgrading software version while retaining user data. The user data directory is where configs, notebooks, and results are stored, this location is ``~/primaite/<version>/`` on linux/darwin and ``C:\\Users\\<username>\\primaite\\<version>`` on Windows.
Episode schedule
The strategy for selecting different variants around the same scenario when advancing from one episode to another in the environment.
Discriminator
A unique string given to extensible components in PrimAITE that allow them to be mapped from a YAML config definition to a simulation class.
Plugin
A python package that extends base PrimAITE classes.