- 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
