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🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX

Results 160 Mava issues
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Update to Connector wrapper and scenarios to bring it in line with the version used in benchmarks. Previously, other agents and targets were marked with binary variables. This has been...

size/M

### Describe the bug The (originally combined) `steps_per_second` logging has been separated for now to enforce logging consistencies across Mava systems, enabling downstream aggregate plotting via `marl-eval`. However, the current...

bug

Will add a more detailed description later.

size/M

## What? Adds integration tests todo: - [ ] fix gigastep wrapper - [ ] fix matrax wrapper - [ ] make github run tests closes #1049

test
maintenance
priority/low
size/L

## What? Modify the evaluator to limit the number of vmapped envs to `arch.num_envs` when the total number of evaluation episodes `arch.num_eval_episodes` exceeds this limit (instead of parallelise all the...

enhancement
priority/high
size/L

## What? Changed the assertions to make sure the num_updates is a multiple of num_evaluations. ## Why? Only num_evaluation * num_updates_per_eval are ran while training which can lead to some...

size/S

### Describe the bug Hello! I am trying to get Mava working to test out the library. Following the `README`, I created a 3.9.10 virtualenv and installed `jax[cuda12_local]` (I have...

bug

## What? Implemented a wrapper for gym environments to facilitate their use in sebulba. ## Why? Integrate Sebulba's architecture due to its effectiveness in scenarios involving non-jitted/non-jax environments. ## How?...

size/L

## What? * Swapped black, flake8 and isort for ruff. * Cleaned up dev deps * Lots of minor formatting changes from the new formatter/linter rules ## Why? Faster and...

size/XXL

### Please describe the purpose of the feature. Is it related to a problem? MAVA currently only supports JAX-based environments. ### Describe the solution you'd like Add support to non-JAX...

enhancement