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decision-transformer-pocket
ARotting/decision-transformer-pocket
decision-transformer-pocket is a machine learning model from ARotting. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Decision Transformer Pocket trains an offline return-conditioned policy on 4,000 mixed-quality corridor trajectories. A nearby claim pays 0.4; the larger reward requires first moving away from it, retrieving a key, cr…
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Updated Jul 30, 2026
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At a glance
- Access
- Public
- Created
- Jul 30, 2026
- Updated
- Jul 30, 2026
- SHA
- 77a41755
Model card
From the Hugging Face model README
Decision Transformer Pocket
Decision Transformer Pocket trains an offline return-conditioned policy on 4,000 mixed-quality corridor trajectories. A nearby claim pays 0.4; the larger reward requires first moving away from it, retrieving a key, crossing a door, and claiming treasure.
The behavior-cloning control sees identical states and actions but no requested return. Evaluation asks each policy to realize both low- and high-return targets from three starting positions.
Verified local result
The 18,371-parameter Decision Transformer selected the nearby reward in 100% of 300 target-0.4 episodes and the key-door treasure in 100% of 300 target-1.0 episodes. The 595-parameter behavior-cloning policy could not switch intention, reaching only 33.3% and 66.7% desired-terminal rates.
uv run python projects/decision-transformer-pocket/train.py
uv run pytest tests/test_decision_transformer_pocket.py
- Created
- Jul 30, 2026
- Updated
- Jul 30, 2026