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mrvictoru/
energydecision-dt

Name: energydecision-dt
Availability: InStock

mrvictoru/AEMO_simulated_trade

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EnergyDecision-DT (Legacy)

⚠️ OUTDATED — Superseded by both the modern v2 pretrained model AND the Stage C standalone DT.

This is the legacy 8×384 pretrained model. It was superseded by mrvictoru/energydecision-dt-v2 (8×768, GQA, RMSNorm, weight-tying), which in turn was superseded by the Stage C standalone DT — a transformer distilled from an honest SDP-planning teacher that beats PPO on all 4 identity surfaces and passes the impact gate:

Surface Stage C DT v2 pretrained (8×768) This model (8×384) PPO
Standard Oct $11,573 $4,991 $2,678 $2,353
Dispatch-matched $35,320 $10,138 $8,242* $22,530
Expanded broad-2024 $34,761 $4,596 $2,459 $19,504

* Phase 1 GRPO result (overfit — collapses to $1,533 on standard surface).

This checkpoint is retained for reproducing the legacy study only. The shipped model is models/aemo/dt/aemo_dt_sdp_jtsoc_fullcorpus.pt.

Model Description

EnergyDecision-DT is a Decision Transformer model trained on simulated battery dispatch data from the Australian Energy Market Operator (AEMO) Frequency Control Ancillary Services (FCAS) market. It models optimal battery dispatch as a sequence prediction problem, conditioning on returns-to-go, observed states, and past actions to predict the next action.

Source code: https://github.com/mrvictoru/energydecision

Key Features

Action Space (9-dim):
- Dim 0: Energy dispatch in [-1, 1] (charge/discharge)
- Dims 1-8: FCAS contingency bids in [0, 1]
State Space (18-dim): Normalized market observations
Context Length: 180 timesteps (looks back ~15 hours of history)

Intended Use

This model is intended for:

Research into offline RL for energy markets
Simulation of battery trading strategies in the AEMO FCAS market
Baseline for comparing decision transformer approaches against traditional RL

It is not intended for live trading without further validation, risk management, and regulatory compliance.

Training Data

Source: AEMO simulated trade dataset
Size: 75,945,600 rows across 2,405 episodes
Source policies: A2C-generated trajectories (legacy FCAS-poor corpus)

Model Architecture

DecisionTransformer(
  (embed_return): Linear(1 -> 384)
  (embed_state): Linear(18 -> 384)
  (embed_action): Linear(9 -> 384)
  (embed_timestep): Embedding(100000 -> 384)
  (blocks): 8x TransformerBlock(
      (ln1): RMSNorm(384)
      (attn): MultiheadAttention(384, 8 heads)
      (ln2): RMSNorm(384)
      (ffn): SwiGLU(384 -> 1536 -> 384)
      (dropout): Dropout(p=0.15)
    )
  (ln_f): RMSNorm(384)
  (predict_action): Linear(384 -> 384) -> GELU -> Linear(384 -> 9) -> Tanh
  (predict_state): Linear(384 -> 18)
  (predict_return): Linear(384 -> 1)
)

Hyperparameters

Parameter	Value
Blocks	8
Hidden dim	384
Attention heads	8
Context length	180
Dropout	0.15
State dim	18
Action dim	9
Discount factor	0.95
Return scale	2.0

Recommended RTG (legacy model)

The legacy model peaks at rtg_value=0.5. This is the inverse of the modern v2 model (which peaks at 0.0).

Usage

python

import torch
from huggingface_hub import hf_hub_download
from decision_transformer import DecisionTransformer

model_kwargs = {
    "state_dim": 18, "act_dim": 9, "n_block": 8,
    "h_dim": 384, "context_len": 180, "n_heads": 8,
    "drop_p": 0.15, "max_timestep": 100000,
}

model_path = hf_hub_download("mrvictoru/energydecision-dt", "aemo_dt_fcas_model.pt")
model = DecisionTransformer(**model_kwargs)
model.load_from_checkpoint(model_path)
model.eval()

Citation

bibtex

@misc{energydecision-dt,
  author = {Victor U},
  title = {EnergyDecision-DT: Decision Transformer for AEMO FCAS Battery Trading},
  year = {2026},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/mrvictoru/energydecision-dt}},
}

Surface	Stage C DT	v2 pretrained (8×768)	This model (8×384)	PPO
Standard Oct	$11,573	$4,991	$2,678	$2,353
Dispatch-matched	$35,320	$10,138	$8,242*	$22,530
Expanded broad-2024	$34,761	$4,596	$2,459	$19,504