Locate and validate the training checkpoint

Restore the run configuration

Activate the training environment and recover the saved variables. The latest link was created when you configured the run:

    

        
        
source $HOME/venvs/mappo/bin/activate
export RUN_DIR=$HOME/mappo_navigation_runs/latest
source "$RUN_DIR/run.env"
cd $HOME/BenchMARL

    

If you want to validate an older run, set RUN_DIR to that run directory before you source run.env.

Find the checkpoint

BenchMARL creates an experiment directory under RUN_DIR with a structure similar to:

    

        
        
$RUN_DIR/
└── mappo_navigation_mlp__<experiment-id>/
    ├── config.pkl
    ├── checkpoints/
    │   └── checkpoint_<frames>.pt
    └── ...

    

List the checkpoints created by the run:

    

        
        
find "$RUN_DIR" -type f -path '*/checkpoints/checkpoint_*.pt' -print

    

Select the most recently written checkpoint from this run:

    

        
        
export CHECKPOINT=$(find "$RUN_DIR" -type f -path '*/checkpoints/checkpoint_*.pt' -printf '%T@ %p\n' | sort -nr | head -1 | cut -d' ' -f2-)

    

Display and verify the path:

    

        
        
echo "$CHECKPOINT"
test -n "$CHECKPOINT" && test -f "$CHECKPOINT" || { echo "No checkpoint found under $RUN_DIR" >&2; exit 1; }
echo "Checkpoint found: $CHECKPOINT"

    

The command stops the shell if no checkpoint exists, which prevents later commands from deriving paths from an empty value.

Keep config.pkl with the checkpoint

Determine the BenchMARL experiment directory:

    

        
        
export SOURCE_EXPERIMENT_DIR=$(dirname "$(dirname "$CHECKPOINT")")

    

Verify both artifacts:

    

        
        
ls -lh "$CHECKPOINT" "$SOURCE_EXPERIMENT_DIR/config.pkl"

    

BenchMARL uses the following relative layout when it reconstructs an experiment:

    

        
        
<experiment-directory>/
├── config.pkl
└── checkpoints/
    └── checkpoint_<frames>.pt

    

Don’t copy only checkpoint_<frames>.pt when you need to reload the complete BenchMARL experiment.

Read deployment metadata from the experiment

Read the actual number of agents stored in the task configuration:

    

        
        
export CHECKPOINT_AGENTS=$(python -c "import pickle; f=open('$SOURCE_EXPERIMENT_DIR/config.pkl','rb'); pickle.load(f); cfg=pickle.load(f); print(cfg['n_agents'])")

    

Verify the read:

    

        
        
echo "Checkpoint agents=$CHECKPOINT_AGENTS"

    

Inspect the full VMAS task configuration:

    

        
        
python -c "import pickle; f=open('$SOURCE_EXPERIMENT_DIR/config.pkl','rb'); task=pickle.load(f); cfg=pickle.load(f); print('Task:', task); print('Task configuration:', cfg)"

    

This value comes from the trained experiment and prevents a checkpoint from being registered with a stale shell value for the agent count.

Save the checkpoint variables so you can restore them after a new SSH login:

    

        
        
declare -px CHECKPOINT SOURCE_EXPERIMENT_DIR CHECKPOINT_AGENTS >> "$RUN_DIR/run.env"

    

Evaluate the reloaded policy

Run BenchMARL’s evaluation entry point against the selected checkpoint:

    

        
        
python benchmarl/evaluate.py "$CHECKPOINT"

    

This command reconstructs the experiment from config.pkl, loads the trained state, and runs the saved evaluation configuration. A successful run proves that BenchMARL can execute the policy, while the earlier file checks prove only that the artifacts exist.

List the CSV logs produced by the experiment:

    

        
        
find "$SOURCE_EXPERIMENT_DIR" -type f -name '*.csv' -print

    

Compare the first and final evaluation returns in the logs. Returns vary with the random seed and package versions, so don’t use an unverified numeric threshold. A flat or falling return means that checkpoint creation succeeded but the policy didn’t demonstrate learning.

What you’ve accomplished and what’s next

You’ve selected a valid checkpoint, confirmed that its matching configuration is present, recovered deployment metadata, and reloaded the policy for evaluation. The checkpoint and config.pkl now form the portable BenchMARL experiment.

Next, you can optionally deploy the experiment to a GUI . If you don’t have the companion GUI code, skip to export the MAPPO actor for inference .