gianexe/leo-collision-avoidance-mappo

LEO collision avoidance with MAPPO

One shared actor, trained with MAPPO, that decides when and how a satellite in low Earth orbit should maneuver to avoid a close approach. Each satellite sees only its single most threatening neighbour. There is no communication and no priority rule between satellites.

Built on OrbitZoo (PettingZoo API with an Orekit propagation backend) as part of a thesis.

Code: https://github.com/gian-gg/marl-mappo-leo-collision-avoidance

Files

File	What it is
v4_stage3_seed42.pt	Final checkpoint (SHA-256 98bba412582ba669…, training seed 42)
eval_stage3.json	Frozen evaluation and training configuration at 150 agents

Model

• Algorithm: MAPPO, shared actor (parameter sharing), centralized critic used in training only.
• Actor: 22 inputs, hidden layers 128 and 64, 7 discrete actions (no burn, prograde, retrograde, radial out, radial in, cross-track either way), about 11,700 weights.
• Perception: k = 1, the single most threatening neighbour.
• Maneuver: 0.5 m/s per action at up to 7 N (Isp 300 s, burn up to 60 s).
• Decision interval 120 s, 25 decisions per episode.
• Training: curriculum of 16, then 64, then 150 agents, each stage starting from the previous actor. About 4 h on an Apple M1 MacBook Air (8 GB). No collisions in training.

Station-keeping is out of scope: the policy only learns to avoid. Drift and the return delta-v are measured, not flown.

Results

Evaluated on a frozen benchmark of 20 held-out episodes per size. All policies play the same episodes and the actor runs deterministically. A close approach is a pass inside 1 km.

Close approaches per episode (share resolved relative to coasting):

Agents	No-op	Rule	This model
16	8.55	0.45 (94.7%)	0.40 (95.3%)
64	43.60	4.30 (90.1%)	3.00 (93.1%)
150	101.20	10.40 (89.7%)	7.45 (92.6%)

• At 64 and 150 agents the improvement over the physics-based rule is significant (paired Wilcoxon p = 0.0021 and 0.0003). At 16 it is not.
• No policy caused a collision at any size.
• Cost: the model spends about 26% more total delta-v than the rule (1.76 against 1.40 m/s at 150 agents, including the calculated return to the slot).
• A second training seed (7) resolves 93.0%. The spread between seeds is about 0.4 close approaches per episode, so smaller differences are not separable from training noise.
• Remaining weakness: of 142 conjunctions at 150 agents where neither satellite maneuvered, 15 were not resolved.

Usage

The checkpoint loads through the thesis code, not a standalone library:

git clone https://github.com/gian-gg/marl-mappo-leo-collision-avoidance
# download v4_stage3_seed42.pt and eval_stage3.json from this repo, then:
oz evaluate --config configs/eval_stage3.json \
  --policy noop --policy rule --policy v4=v4_stage3_seed42.pt --episodes 20

Python 3.10 or 3.11 is required (Python 3.11 recommended); no GPU is needed. The scenario data (data/full/, TLE catalog) is not included here and must be provided as described in the repository.

The .pt file is a PyTorch checkpoint and loading it unpickles data. Only load it with the code above and from this repository.

Limitations

• Trained and evaluated on generated conjunction scenarios built from TLE catalog data, not on real operational events.
• Assumes short-notice-capable propulsion (7 N class, 0.5 m/s per action).
• Discrete maneuver primitives (0.5 m/s, burns of up to 60 s) with a fixed decision interval of 120 s.
• Variation reported is between episodes and across two training seeds only.

Citation

OrbitZoo: Oliveira, Dyreby, Caldas and Soares, "OrbitZoo: Real Orbital Systems Challenges for Reinforcement Learning", arXiv:2504.04160.