EnergyDecision-DT-V2

⚠️ OUTDATED — SUPERSeded by the Stage C standalone DT.

This model (modern v2 pretrained, 8×768 GQA) was the SOTA as of Jul 2026, beating PPO on dispatch-matched ($10,138/ep) and standard ($4,630/ep) surfaces. It has since been superseded by the Stage C standalone DT distilled from an honest SDP-planning teacher, which beats PPO on all 4 identity surfaces and passes the market-impact gate:

Surface	Stage C DT (rtg_mode="auto")	This model (v2 pretrained)	PPO
Standard Oct	$11,573	$4,991	$2,353
Dispatch-matched	$35,320	$10,138	$22,530
Expanded broad-2024	$34,761	$4,596	$19,504
2025 OOD	$25,862	−$694	$14,320
Impact gate	2.6–3.0× PPO	62–83% identity	—

The shipped model is models/aemo/dt/aemo_dt_sdp_jtsoc_fullcorpus.pt (see energydecision repo). This checkpoint is retained for reproducing the Jul 2026 benchmark study.

Model Description

EnergyDecision-DT-V2 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.

The model learns to dispatch battery energy storage (charge/discharge) and bid into 8 FCAS contingency markets simultaneously, using a modern transformer architecture with Grouped-Query Attention, QK-Norm, SwiGLU activations, and weight-tied embeddings.

Key Features

• Modern architecture: Grouped-Query Attention (6 KV heads, 12 Q heads), QK-Norm for training stability, SwiGLU FFN, RMSNorm pre-norm
• Weight tying: Embedding and prediction layers share weights for parameter efficiency
• Action Space (9-dim):
  • Dim 0: Energy dispatch in [-1, 1] (negative = charge, positive = discharge)
  • Dims 1-8: FCAS contingency bids in [0, 1]
• State Space (18-dim): Normalized market observations including prices, demand, renewables penetration, and battery state-of-charge
• Context Length: 210 timesteps (looks back ~17.5 hours of 5-minute dispatch intervals)

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: 86,412,124 rows after filtering
• Episodes: 2,401 episodes (after filtering for minimum context length)
• Source policies: A2C (76.9M rows) + GRPO-DT (11.9M rows)

Model Architecture

DecisionTransformer(
  (embed_return):  Linear(1 -> 768)
  (embed_state):   Linear(18 -> 768)
  (embed_action):  Linear(9 -> 768)
  (embed_timestep): Embedding(100000 -> 768)
  (embed_ln):      RMSNorm(768)
  (blocks): 8x ModernBlock(
    (norm1): RMSNorm(768)
    (attn): CausalSelfAttention(
      q_proj: Linear(768 -> 768)   # 12 Q heads x 64 head_dim
      k_proj: Linear(768 -> 384)   # 6 KV heads x 64 head_dim
      v_proj: Linear(768 -> 384)   # 6 KV heads x 64 head_dim
      out_proj: Linear(768 -> 768)
      qk_norm: RMSNorm(64) per head
      n_rep: 2 (each KV head serves 2 Q heads -- GQA)
    )
    (norm2): RMSNorm(768)
    (ffn): SwiGLU(768 -> 3072 -> 768, dropout=0.15)
  )
  (ln_f): RMSNorm(768)
  (pred_act):   Linear(768 -> 9)  -> Tanh   [tied with embed_act weights]
  (pred_state): Linear(768 -> 18)         [tied with embed_state weights]
  (pred_return): Linear(768 -> 1)         [tied with embed_return weights]
)

Hyperparameters

Training Procedure

• Hardware: NVIDIA GPU (AMP mixed precision)
• Optimizer: AdamW (lr=3e-5, weight_decay=1e-4)
• Batch size: 128
• Epochs: 3
• Gradient clipping: 1.0

Training Metrics

Benchmark Results (historical — Jul 2026)

Evaluated on dispatch-matched (Dalrymple North 8 MWh / 30 MW, Q4 2024 SA1) and standard (5 regions, medium batteries) surfaces:

RTG Calibration (historical)

Usage

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": 768, "n_heads": 12, "n_kv_heads": 6,
    "context_len": 210, "drop_p": 0.15,
    "max_timestep": 100000, "qk_norm": True,
    "rope_enabled": False, "tie_weights": True,
}

model_path = hf_hub_download("mrvictoru/energydecision-dt-v2", "aemo_dt_fcas_model.pt")
model = DecisionTransformer(**model_kwargs)
model.load_from_checkpoint(model_path)
model.eval()

Citation

@misc{energydecision-dt-v2,
  author = {Victor U},
  title = {EnergyDecision-DT-V2: Decision Transformer for AEMO FCAS Battery Trading},
  year = {2026},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/mrvictoru/energydecision-dt-v2}},
}