---

license: gpl-3.0 library_name: pytorch tags:

• minecraft
• combat
• pvp
• transformer
• behavior-cloning
• sequence-modeling
• game-ai

---

minecraft-combat-transformer

Model Details

Model Description

minecraft-combat-transformer is a PyTorch transformer-based sequence model for Minecraft-style combat behavior modeling. It predicts short-horizon combat actions and outcome-related signals from recent gameplay state sequences.

The model is designed for research and experimentation with game AI, combat behavior modeling, imitation learning, and action-policy prediction in a controlled Minecraft PvP-style environment.

It is not a general language model and does not process natural language.

• Developed by: skork
• Model type: Transformer sequence model
• Task type: Game combat action and outcome prediction
• Framework: PyTorch
• License: gpl-3.0
• Finetuned from: Not applicable

Intended Uses

Direct Use

This model may be used for:

• Research into Minecraft-style combat AI
• Studying sequence models for game-action prediction
• Offline evaluation of combat behavior policies
• Controlled/private PvP bot experiments
• Imitation-learning and policy-modeling experiments

Downstream Use

The checkpoint may be integrated into a larger game-AI system that handles feature extraction, inference, action arbitration, and environment interaction.

This model is only one component of such a system. It does not include a full Minecraft client, server integration, anti-cheat bypass, or deployment runtime.

Out-of-Scope Use

This model is not intended for:

• Violating server rules or terms of service
• Cheating on public servers
• Evading anti-cheat systems
• Harassing players or disrupting online communities
• Use in environments where automated gameplay is not permitted

Users are responsible for ensuring that any use of this model follows the rules of the environment where it is used.

Bias, Risks, and Limitations

The model reflects the gameplay data and training objectives used to train it. It may inherit weaknesses, habits, or blind spots from the source data.

Known limitations:

• Performance may not generalize to different Minecraft versions, combat mechanics, latency conditions, clients, or server rules.
• The model may behave poorly if the input feature schema does not match the training schema.
• The checkpoint requires compatible inference code and feature preprocessing.
• The model predicts actions and combat signals; it does not independently understand game rules or server policy.
• Rare combat events may be less reliable than common movement or aiming-related signals.
• Live performance depends heavily on the surrounding action-selection and deployment system.

Recommendations

Use this model only in private, local, or explicitly permitted environments. Evaluate carefully before deployment. Do not assume the model will perform safely or effectively outside the conditions it was trained and tested on.

How to Get Started

This is a custom PyTorch checkpoint, not a Hugging Face AutoModel.

Example checkpoint inspection:

import torch

ckpt = torch.load("newgen6.pt", map_location="cpu", weights_only=False)

print(ckpt.keys())
print(ckpt["config"].keys())
print(len(ckpt["feature_names"]))

Expected checkpoint contents include:

model_state
config
mean
std
feature_names

A compatible model definition and preprocessing pipeline are required to run inference.

Training Details

Training Data

The model was trained on privately collected Minecraft-style PvP gameplay telemetry. The data included sequence windows of combat state features, player/opponent movement, attack timing, and short-horizon combat outcome labels.

The public checkpoint does not include raw training logs, player identifiers, session records, packet data, match IDs, usernames, UUIDs, or local file paths.

Training Procedure

The model was trained using supervised sequence modeling with behavior-cloning style action heads and auxiliary combat-outcome prediction heads.

The training setup used:

• Transformer sequence encoder
• Multi-head action prediction
• Multi-head future outcome prediction
• Held-out opponent validation split
• Early stopping by custom validation score
• Best-checkpoint restoration

Training Hyperparameters

Approximate training configuration:

• Sequence length: 96
• Model dimension: 192
• Transformer layers: 5
• Attention heads: 6
• Feed-forward size: 640
• Batch size: 1024
• Precision: bfloat16 mixed precision
• Training regime: Supervised multi-task sequence learning
• Checkpoint selected: Best validation score checkpoint

Evaluation

Testing Data

Evaluation was performed on a held-out opponent split from the training corpus. Opponents in the validation split were separated from training opponents to estimate generalization to unseen combat behavior.

Metrics

The model was evaluated using a combination of:

• Accuracy
• Macro F1
• Average Precision
• ROC AUC
• Custom aggregate validation score

The aggregate score prioritized movement imitation, attack prediction, jump prediction, and combat-outcome AUC, with a smaller penalty for validation loss.

Results

Best checkpoint:

Best epoch: 8
Best validation score: 2.8908

Selected held-out metrics:

forward opp-human macroF1: 0.873
strafe opp-human macroF1: 0.816
attack_now AP: 0.868
jump AP: 0.296
sprint AUC: 0.986
opp_aim_lock AUC: 0.994
commit_punish AUC: 0.969
space_safe AUC: 0.981
sprint_crit_incoming AUC: 0.993

These metrics are specific to the private evaluation split and should not be interpreted as universal performance guarantees.

Technical Specifications

Architecture and Objective

The model is a transformer-based sequence model trained on fixed-length windows of combat features. It outputs multiple prediction heads, including movement/action heads and auxiliary heads for short-horizon combat outcomes.

The objective combines action imitation with grounded outcome-prediction losses.

Input

The model expects normalized numerical feature sequences using the same feature order and normalization statistics stored in the checkpoint. -> MINECRAFT TICKS

Output

The model outputs action and combat-signal predictions through multiple heads. Exact output interpretation requires the matching inference code and configuration.

Environmental Impact

Environmental impact was not formally measured.

• Hardware: CUDA-capable GPU
• Precision: bfloat16 mixed precision
• Carbon emitted: Not measured

Citation

No formal paper is associated with this release.

License

This model is released under the gpl-3.0 License.

Model Card Contact

No public contact information provided.