#2266 - Finally started to add some user documentation around how to properly create networks
This commit is contained in:
Chris McCarthy
@@ -13,42 +13,12 @@ This section configures how PrimAITE saves data during simulation and training.
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.. code-block:: yaml
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io_settings:
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save_final_model: True
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save_checkpoints: False
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checkpoint_interval: 10
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# save_logs: True
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# save_transactions: False
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save_agent_actions: True
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save_step_metadata: False
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save_pcap_logs: False
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save_sys_logs: False
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``save_final_model``
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--------------------
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Optional. Default value is ``True``.
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Only used if training with PrimaiteSession.
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If ``True``, the policy will be saved after the final training iteration.
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``save_checkpoints``
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--------------------
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Optional. Default value is ``False``.
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Only used if training with PrimaiteSession.
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If ``True``, the policy will be saved periodically during training.
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``checkpoint_interval``
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-----------------------
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Optional. Default value is ``10``.
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Only used if training with PrimaiteSession and if ``save_checkpoints`` is ``True``.
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Defines how often to save the policy during training.
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``save_logs``
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-------------
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@@ -31,7 +31,13 @@ To see the configuration for these nodes, refer to the following:
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:maxdepth: 1
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:glob:
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simulation/nodes/*
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simulation/nodes/computer
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simulation/nodes/firewall
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simulation/nodes/router
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simulation/nodes/server
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simulation/nodes/switch
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simulation/nodes/wireless_router
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simulation/nodes/network_examples
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``links``
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---------
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@@ -54,15 +60,13 @@ this results in:
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.. code-block:: yaml
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links:
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- ref: computer_1___switch
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endpoint_a_ref: computer_1
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- endpoint_a_hostname: computer_1
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endpoint_a_port: 1 # port 1 on computer_1
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endpoint_b_ref: switch
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endpoint_b_hostname: switch
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endpoint_b_port: 1 # port 1 on switch
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- ref: computer_2___switch
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endpoint_a_ref: computer_2
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- endpoint_a_hostname: computer_2
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endpoint_a_port: 1 # port 1 on computer_2
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endpoint_b_ref: switch
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endpoint_b_hostname: switch
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endpoint_b_port: 2 # port 2 on switch
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``ref``
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@@ -70,7 +74,7 @@ this results in:
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The human readable name for the link. Not used in code, however is useful for a human to understand what the link is for.
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``endpoint_a_ref``
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``endpoint_a_hostname``
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^^^^^^^^^^^^^^^^^^
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The ``hostname`` of the node which must be connected.
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@@ -78,10 +82,10 @@ The ``hostname`` of the node which must be connected.
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``endpoint_a_port``
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^^^^^^^^^^^^^^^^^^^
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The port on ``endpoint_a_ref`` which is to be connected to ``endpoint_b_port``.
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The port on ``endpoint_a_hostname`` which is to be connected to ``endpoint_b_port``.
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This accepts an integer value e.g. if port 1 is to be connected, the configuration should be ``endpoint_a_port: 1``
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``endpoint_b_ref``
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``endpoint_b_hostname``
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^^^^^^^^^^^^^^^^^^
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The ``hostname`` of the node which must be connected.
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@@ -89,5 +93,5 @@ The ``hostname`` of the node which must be connected.
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``endpoint_b_port``
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^^^^^^^^^^^^^^^^^^^
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The port on ``endpoint_b_ref`` which is to be connected to ``endpoint_a_port``.
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The port on ``endpoint_b_hostname`` which is to be connected to ``endpoint_a_port``.
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This accepts an integer value e.g. if port 1 is to be connected, the configuration should be ``endpoint_b_port: 1``
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@@ -0,0 +1,38 @@
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.. only:: comment
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© Crown-owned copyright 2023, Defence Science and Technology Laboratory UK
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.. _network_examples:
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``Network Examples``
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====================
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The below examples demonstrate how to configure different types of network in PrimAITE. They examples all have network
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topology diagrams. Each rectangle represents a single Node, with the hostname inside of the rectangle. Physical inks are
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represented by lines between two nodes. At each end of the line is the network interface number on the node the link is
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connected to. Where the network interface is alsoa layer-3 device, the label also contains the ip address and subnet
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mask in CIDR format (``<ip address>/<no mask bits>``). All network diagrams on this page use the following node type
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colour key:
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.. image:: images/primaite_node_type_colour_key.png
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:width: 300
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:align: center
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#1. Client-Server P2P Network
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-----------------------------
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This example demonstrates how to create a minimal two-node client-server P2P network. the network consists of a Computer
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and a Server on the same subnet with a single Link connecting the two.
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.. image:: images/primaite_example_client_server_p2p_network.png
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:width: 800
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:align: center
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#2. Basic Switched Network
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--------------------------
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In this example we'll create a basic switched network. The network will consist of two Computers, a Server, and a
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Switch, all on the same subnet.
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@@ -1,75 +0,0 @@
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.. only:: comment
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© Crown-owned copyright 2023, Defence Science and Technology Laboratory UK
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``training_config``
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===================
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Configuration items relevant to how the Reinforcement Learning agent(s) will be trained.
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``training_config`` hierarchy
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-----------------------------
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.. code-block:: yaml
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training_config:
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rl_framework: SB3 # or RLLIB_single_agent or RLLIB_multi_agent
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rl_algorithm: PPO # or A2C
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n_learn_episodes: 5
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max_steps_per_episode: 200
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n_eval_episodes: 1
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deterministic_eval: True
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seed: 123
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``rl_framework``
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----------------
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The RL (Reinforcement Learning) Framework to use in the training session
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Options available are:
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- ``SB3`` (Stable Baselines 3)
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- ``RLLIB_single_agent`` (Single Agent Ray RLLib)
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- ``RLLIB_multi_agent`` (Multi Agent Ray RLLib)
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``rl_algorithm``
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----------------
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The Reinforcement Learning Algorithm to use in the training session
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Options available are:
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- ``PPO`` (Proximal Policy Optimisation)
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- ``A2C`` (Advantage Actor Critic)
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``n_learn_episodes``
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--------------------
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The number of episodes to train the agent(s).
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This should be an integer value above ``0``
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``max_steps_per_episode``
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-------------------------
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The number of steps each episode will last for.
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This should be an integer value above ``0``.
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``n_eval_episodes``
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-------------------
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Optional. Default value is ``0``.
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The number of evaluation episodes to run the trained agent for.
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This should be an integer value above ``0``.
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``deterministic_eval``
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----------------------
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Optional. By default this value is ``False``.
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If this is set to ``True``, the agents will act deterministically instead of stochastically.
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``seed``
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--------
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Optional.
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The seed is used (alongside ``deterministic_eval``) to reproduce a previous instance of training and evaluation of an RL agent.
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The seed should be an integer value.
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Useful for debugging.
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