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Data Ordering Consistency in MASAC Algorithm - Critical for Custom Critic Implementation

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Python
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Description

I'm using BenchMARL for my project with a custom environment and custom actor/critic models. My setup uses MASAC algorithm with continuous, centralized environment containing 3 agents in a single group with share_param_critic=False (each agent has its own critic).
In my critic model design, each critic needs to see all agents' states and actions. My critic has 3 input heads:

Head 1: Current agent's state + action
Head 2: Other agent's state + action
Head 3: Other agent's state + action

For this to work correctly, I need to know the exact ordering of data passed to the forward function.
I receive global_action through keys sent by MASAC and construct global_state from observations (following MPE example pattern).
My critical question: Does the data ordering remain consistent throughout the algorithm pipeline (algorithm → buffer → loss computation in TorchRL)?
Expected ordering:

[act0, act1, act2] for actions
[obs0, obs1, obs2] for observations

I attempted to test this ordering by creating a fixed-action actor and fixed-state task, but couldn't reach a definitive answer. Since this is critical for my project's correctness, I decided to ask the team directly.
Environment Details

Algorithm: MASAC
Environment: Custom continuous, centralized
Agents: 3 agents, single group
Critic: Individual critics (share_param_critic=False)
Architecture: Each critic processes global state + global actions

Specific Request
Can you confirm that the data ordering [agent0, agent1, agent2] remains consistent throughout the entire training pipeline, or does it change at any point during algorithm execution, buffer storage, or loss computation?
Thank you for your attention and cooperation.

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Research direction

Trace the MASAC algorithm, buffer storage, and TorchRL loss-computation entry points to identify how agent observations and actions are ordered. Verify whether [agent0, agent1, agent2] is preserved throughout training, and document the confirmed ordering or the point where it changes.

Written by the indexing model from the issue text.

Assessment

Tech stack
python, pytorch
Domain
machine-learning
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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