This is a repository containing deep RL agents for NASimEmu.
Related workshop paper, and dissertation (TBD).
Install NASimEmu and run training as:
python main.py <path-to-scenario>
Alternatively, you can test a trained model as an example below:
mkdir out/
python main.py -load_model trained_models/mlp.pt --trace ../NASimEmu-public/scenarios/uni.v2.yaml -device cpu -net_class NASimNetMLP -use_a_t -episode_step_limit 100 -augment_with_action
Generalization of invariant architectures vs. MLP:
Training to stop:
Last action embedding:
Comparison of architecture variants:
Scaling experiment:
main.py huge-gen-rgoal-stoch -device cpu -cpus 2 -epoch 100 -max_epochs 200 --no_debug -net_class NASimNetInvMAct -force_continue_epochs 100 -use_a_t -episode_step_limit 200 -augment_with_action
Simulation-trained agents can be transferred to emulation, see the emulation log.