Causal GPT-RL — Pyramids
A Causal GPT-RL policy trained for the Unity ML-Agents release-23 Pyramids environment.
This repository contains one self-contained ONNX policy:
pyramids/pyramids-b16.onnx
The graph includes observation normalization and can run with ONNX Runtime or Unity Sentis without PyTorch at inference time.
Training data
This policy was trained using:
unity/pyramids/simple-v0unity/pyramids/medium-v0
The unity/pyramids/expert-v0 dataset was not used for training and is included
below only as an evaluation reference.
Evaluation
| Policy | Context | Batch | Observation | Action | Return | Completed |
|---|---|---|---|---|---|---|
pyramids/pyramids-b16.onnx |
32 | 16 | 172 | Discrete(5) |
1.7738 ± 0.1278 | 128/128 |
The result is the mean ± population standard deviation over 128 episodes:
16 agents across eight fresh Unity launches using environment seeds 100..107.
All episodes completed successfully on the matching model-removed ML-Agents release-23 Pyramids build.
Dataset reference
| Dataset | Episodes | Mean return | Training use |
|---|---|---|---|
unity/pyramids/simple-v0 |
3,042 | 1.6256 ± 0.4154 | Used |
unity/pyramids/medium-v0 |
4,159 | 1.7432 ± 0.2601 | Used |
unity/pyramids/expert-v0 |
5,348 | 1.7953 ± 0.2661 | Not used |
The ONNX policy exceeds the mean returns of both training datasets. It is also approximately 1.2% below the expert dataset mean, despite never being trained on the expert dataset.
Dataset returns are sums of the recorded per-step rewards for each Minari episode.
ONNX interface
| Input/output | Shape |
|---|---|
states |
[16, 32, 172] |
actions |
[16, 32, 5] |
is_bos |
[16, 32, 1] |
mask |
[16, 32] |
action |
[16, 5] |
The four observation sensors must be concatenated in this order:
56 + 56 + 56 + 4
action contains five logits. Select the environment action with argmax, then
feed its five-way one-hot representation into the next action window. Maintain
one independent 32-step rolling context per agent.
The graph uses the checkpoint's bos_cache_mode=discard serving convention.
Companion resources
- Unity environment build
- Recorded datasets
- Evaluation code
- Causal GPT-RL models for Gymnasium and MuJoCo
License
Released under PolyForm Noncommercial License 1.0.0. See LICENSE for details.
For commercial licensing, contact the maintainers via
ccnets.org.