- Fixed all errors that were caused b fixing the above.
- Some tests still fail, these are for SS to fix.
- Dropped the old run_generic stuff from conftest.py
## Summary
- Added the fix from #1535 with minor changes to make sure that the `primaite_env.step()` function can properly parse the action
- added the config deterministic and seed to training config
- added the deterministic and seed to the Training config class, with defaults `False` and `None` respectively
- minor fix to `primaite_env.close()` function so that it now works
## Test process
Added e2e tests for generic, ppo and a2c which evaluates a trained agent twice to make sure that the seeding and deterministic action works
## 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
#1386: added the ability to set deterministic and seeding RNG when training and evaluating + the fix provided in #1535
Related work items: #1386, #1535
## Summary
* Made RLlib and SB3 agents save at the end of each learning session by default using a common file naming format. Also now agents only checkpoint every n and not on the final episode.
## Test process
*Tests saved agent file in the test_primaite_session test.
## 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
- [ ] 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: #1593
## Summary
As per the discussion this morning, this PR reimplements changes that were made by ADSP to make the default rewards smaller. This also adds type hints rewards as floats.
## Test process
I checked that sessions are able to run and that they report values similar to what we are used to but smaller by a factor of 10000. I did not change the reward values in the integration test configs, and the tests still pass.
## 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: #889, #1586
- Added test_seeded_learning test and test_deterministic_evaluation test.
- Passed config values seed and deterministic to ppo agent
- Dropped deterministic override in evaluate functions
- TempPrimaiteSession now writes files to a UUID folder rather than datetime
- Added seed to Ray RLlib agent setup in rllib.py
- Added seed to SB3 agent setup in sb3.py
## Summary
* Brought over the RLlib, hardcoded agents, and simple agents from ADSP 1.1.0. This opened a can of worms... ADSP got their stuff working in notebooks (***_stares at data scientists!_** 😂) but hadn't integrated it into the PrimAITE package or made the other PrimAITE functionality work with it.
* RLlib agents have been fully integrated with the wider PrimAITE package. This was done by:
* The creation of an `AgentSessionABC` and `HardCodedAgentSessionABC` classes.
* `SB3Agent` and `RLlibAgent` classes then inherited from `AgentSessionABC`.
* The ADSP hardcoded agents were integrated into subclasses of `HardCodedAgentSessionABC`.
* The random and dummy agents were also integrated into subclasses of `HardCodedagentSessionABC`.
* A set of session output directories were created and managed by the agent session to enable consistent storage of session outputs in a common format regardless of the agent type.
* The main config was rafactored so that it had
* **agent_framework** - To identify whether SB3, RLlib, or Custom.
* **agent_identifier** - To identify whether PPO, A2C, hardcoded, random, or dummy.
* **deep_learning_framework** - To identify which framework to use for RLlib.
* Transactions have been overhauled to simplify the process. It also means that they're written in real time so they're not lost if the agent crashes.
* Tests completely overhauled to use `PrimaiteSession`, or at least a test subclass, `TempPrimaiteSession`. It's temp because it uses temp directory rather than main primaite session directory, and it cleans up after itself.
* All the crap removed from `main.py` and made it so that it just runs `PrimaiteSession`.
Now this is where I went off on a tangent...
* CLI added to just make my life and everyone else's life easier.
* Primaite app config added to hold things like logging format, levels etc.
* A `primaite.data_viz.session_plots` module added so that the average reward per episode for each session is plotted and saves for each session (this helped while we were testing and bug fixing).
## Test process
* All tests use `TempPrimaiteSession`, which uses `PrimaiteSession`.
* I still need to write a tests that runs the RLlib, hardcoded, and random/dummy agents. I'll do that now while this is being reviewed.
## Still to do
* Update docs. I'm getting this PR up now so we can get it in to make use of the features. I'll get the docs updated today either on this branch or another branch (depending on how long this review takes).
## 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
- [ ] 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: #917, #1563
## Summary
Just splits the install primaite step into two depending if agent is using windows or not.
## Test process
Ran a build successfully.
## Checklist
- [ ] This PR is linked to a **work item**
- [ ] 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
- [ ] I have run **pre-commit** checks for code style
## Summary
Ported over ADSP changes regarding the randomised red agent.
Red agent currently only works on laydown configs which contain links.
Each episode generates random red agent instructions
## Test process
Written a test that ensures that the random red agent produces random red agent instructions
| Random red agent | Laydown | Agent Identifier | Run 1 | Run 2 | Run 3 |
|------------------|------------------------|------------------|------------------------------------------------------------------------------------|------------------------------------------------------------------------------------|------------------------------------------------------------------------------------|
| NONE | Very Basic (Laydown 3) | A2C |  |  |  |
| RANDOM | Very Basic (Laydown 3) | A2C |  |  |  |
| NONE | Very Basic (Laydown 3) | PPO |  |  |  |
| RANDOM | Very Basic (Laydown 3) | PPO ...
