Description

I have added a new agent -- PPO + LSTM, together with the new EpisodicRolloutBuffer, which is similar to VanillaRolloutBuffer but samples entire trajectories instead of random transitions in order to train the LSTM appropriately.

I have also added an example notebook to train it on Atari - Space Invaders, which achieves the following results:

In this case, it performs very similarly to vanilla PPO:

Motivation and Context

PPO LSTM can achieve better performance than PPO in partially observed environments.

• I have raised an issue to propose this change (required for new features and bug fixes)

Types of changes

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)
• Documentation (update in the documentation)

Checklist

• I've read the CONTRIBUTION guide (required)
• I have updated the changelog accordingly (required).
• My change requires a change to the documentation.
• I have updated the tests accordingly (required for a bug fix or a new feature).
• I have updated the documentation accordingly.
• I have opened an associated PR on the rllte-hub repository (if necessary)
• I have reformatted the code using make format (required)
• I have checked the codestyle using make check-codestyle and make lint (required)
• [] I have ensured make pytest and make type both pass. (required)
• I have checked that the documentation builds using make doc (required)

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