Added GFX license conditions. Included LICENSE file in build. Fixed a few character issues in README.md

LICENSE

@@ -0,0 +1,28 @@
+GFX License
+
+GFX Conditions
+
+These GFX conditions confirm the provision of the following artefacts as GFX by Defence Science and Technology
+Laboratory UK (DSTL) to QinetiQ Training and Simulation Ltd (QTSL) (and subcontractors engaged in activity on task by
+request to the QQ mailbox):
+
+- PrimAITE Overview
+- Access to PrimAITE & user instructions
+- Track 2 tech support
+
+Suppliers will be required to sign up to the QTSL Collaborative Working Environment (CWE) SyOPs and fill out a User
+Access Request Form. Provided they have a minimum of Cyber Essentials, and the user has the required clearance, they
+will then be provided with credentials to access the site by QQ.
+
+DSTL mandate that any changes made to the PrimAITE source code be passed back to QTSL (during or on termination of the
+task) so that QQ can capture any potential enhancements to PrimAITE.
+
+This contains OFFICIAL information to be used to inform work on ARCD tasks (under SERAPIS).
+
+The material is supplied in confidence to QQ and their subcontractors under SERAPIS, and is issued to inform only those
+that need to know its contents in the course of their official duties whilst engaged in activities under the contract.
+The material consists of proprietary information which is the property of the Crown. The information contained within
+the material may constitute valuable technical information and be commercially sensitive in relation to third parties;
+therefore it may not be used or copied for any non-Governmental or commercial purpose without the prior written consent
+of DSTL. The material must be stored and protected appropriately.All material must be destroyed at the end of the task.
+Please note the contractual obligations relating to provision of these materials.

README.md

@@ -1,38 +1,38 @@
 # PrimAITE
 
-The ARCD Primary-level AI Training Environment (PrimAITE) provides an effective simulation capability for the purposes of training and evaluating AI in a cyber-defensive role. It incorporates the functionality required of a primary-level ARCD environment, which includes: 
+The ARCD Primary-level AI Training Environment (**PrimAITE**) provides an effective simulation capability for the purposes of training and evaluating AI in a cyber-defensive role. It incorporates the functionality required of a primary-level ARCD environment, which includes:
 
-- The ability to model a relevant platform / system context; 
+- The ability to model a relevant platform / system context;
 
-- The ability to model key characteristics of a platform / system by representing connections, IP addresses, ports, traffic loading, operating systems, services and processes; 
+- The ability to model key characteristics of a platform / system by representing connections, IP addresses, ports, traffic loading, operating systems, services and processes;
 
 - Operates at machine-speed to enable fast training cycles.
 
-PrimAITE presents the following features: 
+PrimAITE presents the following features:
 
-- Highly configurable (via YAML files) to provide the means to model a variety of platform / system laydowns, mission profiles and adversarial attack scenarios; 
+- Highly configurable (via YAML files) to provide the means to model a variety of platform / system laydowns, mission profiles and adversarial attack scenarios;
 
-- A Reinforcement Learning (RL) reward function based on (a) the ability to counter the specific modelled adversarial cyber-attack, and (b) the ability to ensure mission success; 
+- A Reinforcement Learning (RL) reward function based on (a) the ability to counter the specific modelled adversarial cyber-attack, and (b) the ability to ensure mission success;
 
-- Provision of logging to support AI evaluation and metrics gathering; 
+- Provision of logging to support AI evaluation and metrics gathering;
 
-- Uses the concept of Information Exchange Requirements (IERs) to model background pattern of life, adversarial behaviour and mission data (on a sliding scale of criticality); 
+- Uses the concept of Information Exchange Requirements (IERs) to model background pattern of life, adversarial behaviour and mission data (on a sliding scale of criticality);
 
-- An Access Control List (ACL) function, mimicking the behaviour of a network firewall, is applied across the model, following standard ACL rule format (e.g. DENY/ALLOW, source IP address, destination IP address, protocol and port);  
+- An Access Control List (ACL) function, mimicking the behaviour of a network firewall, is applied across the model, following standard ACL rule format (e.g. DENY/ALLOW, source IP address, destination IP address, protocol and port);
 
-- Application of IERs to the platform / system laydown adheres to the ACL ruleset; 
+- Application of IERs to the platform / system laydown adheres to the ACL ruleset;
 
-- Presents an OpenAI gym or RLLib interface to the environment, allowing integration with any OpenAI gym compliant defensive agents;  
+- Presents an OpenAI gym or RLLib interface to the environment, allowing integration with any OpenAI gym compliant defensive agents;
 
-- Full capture of discrete logs relating to agent training (full system state, agent actions taken, instantaneous and average reward for every step of every episode)​; 
+- Full capture of discrete logs relating to agent training (full system state, agent actions taken, instantaneous and average reward for every step of every episode);
 
-- NetworkX provides laydown visualisation capability.  
+- NetworkX provides laydown visualisation capability.
 
 ## Getting Started with PrimAITE
 
 ### 💫 Install & Run
 **PrimAITE** is designed to be OS-agnostic, and thus should work on most variations/distros of Linux, Windows, and MacOS.
-Currently, the PRimAITE wheel can only be installed from GitHub. This may change in the future with release to PyPi.
+Currently, the PrimAITE wheel can only be installed from GitHub. This may change in the future with release to PyPi.
 
 #### Windows (PowerShell)
 

pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta"
 name = "primaite"
 description = "PrimAITE (Primary-level AI Training Environment) is a simulation environment for training AI under the ARCD programme."
 authors = [{name="Defence Science and Technology Laboratory UK", email="oss@dstl.gov.uk"}]
-license = {text = "GFX"}
+license = {file = "LICENSE"}
 requires-python = ">=3.8, <3.11"
 dynamic = ["version", "readme"]
 classifiers = [
@@ -47,6 +47,7 @@ readme = {file = ["README.md"]}
 [tool.setuptools]
 package-dir = {"" = "src"}
 include-package-data = true
+license-files = ["LICENSE"]
 
 
 [project.optional-dependencies]