A small Decision Transformer that predicts target-SOC waypoints for the hierarchical planning teacher used to build the corpus. The teacher is a two-stage hierarchy: 1. This model predicts a coarse target-SOC waypoint trajectory (K = 8 waypoints) over the episode. 2. The honest SDP executor (src/aemosdpexecutor.py) plans energy dispatch between waypoints under a seasonal price forecast. 3. A greedy FCAS bidder allocates residual headroom to the highest-priced raise/lower service each step. The resulting (observation, action, reward) triples form the SDP-teacher corpus. targets produced from perfect-foresight Oracle LP paths. - Label generator (GitHub, not shipped here): The label dataset is…
Independent publisher
Victor U
mrvictoru
reinforcement learning
Models in Library2
Datasets in Library0
Models on Hugging Face7
Followers1
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Published tasks: Reinforcement learning.
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Models
The shipped / preferred model of the energydecision benchmark. A standalone Decision Transformer distilled from an honest (non-clairvoyant), degradation-aware SDP-planning teacher — no solver in the control loop (the Jt(soc) prompt is precomputed) — that beats the PPO reference on all four identity surfaces and remains strong under the market-impact benchmark. (Removed 2026-09: the behaviour-cloning-era files aemodtfcasmodel.pt and aemodtfcasbestcheckpoint.pt.) All four identity surfaces beat PPO with paired-difference 95% CIs excluding zero. Methodology, the physics fixes, and the full experiment ladder: report.md §8.2.10/§8.2.11 and docs/knownissues.md. The model card's numbers above are…