- Removed bool apply_implicit_rule
- Set default implicit_rule to EXPLICIT DENY
- Added position to ACLs in laydown configs
- Removed apply_implicit_rule from training configs
- Added check in access_control_list.py which sets implicit permission to NA if boolean is False
- Changed the defaults in training_config.py so that each scenario has an EXPLICIT ALLOW rule as default implicit rule
- Updated the test_seeding_and_deterministic_session.py because of change no2 adds an extra rule to that scenario
- Added comments in access_control_list.py
- Changed obs_shape to max_number_acl_rules from max_number_acl_rules + 1 as index starts from 1
- Commented episode and step print line from test_single_action_space.py
- Re-added the hard-coded mean rewards per episode values from a rpe-trained agent to the deterministic test in test_seeding_and_deterministic_session.py
- Partially tidies up some tests in test_observation_space.py; Still some work to be done on this at a later date.
## Summary
* Update observation space information and standardise formatting of code blocks in that section
* Remove non-ascii quotation characters
* Update custom blue agent page to match new AgentSession classes.
* Introduce glossary
* Provide a first draft of migration guide for 1.2 to 2.0 (probably not comprehensive)
## Test process
Sphinx is able to build the documentation as checked on my local machine
## Checklist
- [x] This PR is linked to a **work item**
- [x] I have performed **self-review** of the code
- [ ] I have written **tests** for any new functionality added with this PR
- [x] I have updated the **documentation** if this PR changes or adds functionality
- [x] I have run **pre-commit** checks for code style
Related work items: #1602
## Summary
Training configs now have 2 different types of episode and step counts - one for train and one for evaluation.
`num_train_episodes`
`num_train_steps`
`num_eval_episodes`
`num_eval_steps`
## Test process
A test file `test_train_eval_episode_steps.py` has been implemented which runs train and evaluation session on two particular configs.
The train and evaluation sessions have different episodes and step count and the test checks that the output log files have the correct number of `total_steps` and `total_episodes`.
## Checklist
- [X] This PR is linked to a **work item**
- [X] I have performed **self-review** of the code
- [X] I have written **tests** for any new functionality added with this PR
- [X] I have updated the **documentation** if this PR changes or adds functionality
- [X] I have run **pre-commit** checks for code style
Related work items: #1566, #1589
## Summary
Unfortunately, I had to do away with the nice and neat matrix strategy for builds, because they do not support conditionals. Instead, I manually replicated the behaviour of the matrix but added a conditional to run every platform only when the 'build reason' is PR.
## Test process
*How have you tested this (if applicable)?*
## Checklist
- [x] This PR is linked to a **work item**
- [x] I have performed **self-review** of the code
- [ ] I have written **tests** for any new functionality added with this PR
- [ ] I have updated the **documentation** if this PR changes or adds functionality
- [x] I have run **pre-commit** checks for code style
Related work items: #1603
## Summary
Since we added File System State as a new part of the observation space, some of the assumptions made by imported ADSP code were not met. This is addressed by these changes.
The code no longer crashes, but the hardcoded ACL agent doesn't work very well, it keeps returning action 0 and receives a low reward. Also if there are ACL rules with 'ANY' as a source IP, it crashes the function `get_node_of_ip` within the HardCodedACLAgent._calculate_action_full_view() method.
I'm not sure how much effort we need to spend fixing the hardcoded agents as they don't seem like they were delivered in a finished state.
## Test process
Can confirm the hardcoded agent can run within a primaite session now.
## Checklist
- [x] This PR is linked to a **work item**
- [x] I have performed **self-review** of the code
- [ ] I have written **tests** for any new functionality added with this PR
- [ ] I have updated the **documentation** if this PR changes or adds functionality
- [x] I have run **pre-commit** checks for code style
## note
I would appreciate some input about what we should do with hardcoded agents for release 2.0.0, it may require significant effort to get them working correctly.
Related work items: #1587
