Training

Usage

Training a New Model

To train a new model, specify the algorithm's configuration file. For example, to train MAPPO:

uv run examples/marl/example/train.py  --config examples/marl/example/config/mappo.yaml  --method mappo

Tip: To reduce memory usage during training, it is highly recommended to disable unnecessary logs in the Unreal Engine console (press ` to open) by running:

log LogTemp off
log LogTongSimGRPC off

Available Algorithms: - MAPPO: example/config/mappo.yaml - IPPO: example/config/ippo.yaml

Configuration: You can customize training parameters (e.g., learning rate, network size, environment settings) by editing the corresponding .yaml file.

Output: Trained models and logs are saved by default in the models/ and logs/ directories, respectively. You can monitor training progress using TensorBoard:

tensorboard --logdir logs

Training Results

Below is a sample reward curve from a training session, showing the model's learning progress over time:

Performance Comparison:

The following table shows the performance comparison of different baseline algorithms on the MACS task. Note that due to the inherent stochasticity of multi-agent environments, the reported performance metrics may exhibit minor fluctuations; these results are primarily intended to verify the effectiveness of the environment and provide a baseline for comparison.

Method	Average Reward per Step	Average Reward
MAPPO	0.038	19.24
IPPO	0.030	14.75
Random	-0.013	-6.51

Evaluating a Pre-trained Model

To evaluate the latest saved model for a given configuration, add the --test flag:

uv run examples/marl/example/train.py  --config examples/marl/example/config/mappo.yaml --test --method mappo

The evaluation script will load the most recent checkpoint from the models/ directory and run it in a test environment without further training.