This repository contains the official trained policies for The files are organized by task environment and training seed: The play.py script in the JoSE repository downloads a pretrained model and executes it in the corresponding environment, allowing agent behaviour to be visualized. The script is called from the repo root as follows: Seeds vary by hand and task. See the HuggingFace model directory to see which seeds are available for each task-hand combination.
Chronoquazicalar is an open-weight model for reinforcement learning from Turbo, released under MIT License. Its published files total 71.9 MB.
Autonomous reinforcement learning policy and value network (~9.65M parameters) for Pokémon Showdown Gen 9 Random Battles.
Parameters—
Context—
Weights16.2 KB
Licensemit
AccessOpen weights
Monthly Downloads—
Model Card
By Turbo, published under mit, revision d7d83d08bffc.
Autonomous reinforcement learning policy and value network (~9.65M parameters) for Pokémon Showdown Gen 9 Random Battles. - Embedding Dimension (dmodel): 256 - Feed-Forward Dimension (dff): 1024 - Value Head (Scalar valuation in [-1, 1]) - Opponent Prediction Head (Simultaneous action prediction) - checkpointlatest.pkl: Model parameters trained via PPO self-play and benchmark sparring. - metrics.json: Latest training metrics, update count, and turn counters.
Read Turbo's full model card
Chronos (Gen 9 Random Battles)
Autonomous reinforcement learning policy and value network (~9.65M parameters) for Pokémon Showdown Gen 9 Random Battles.
- GitHub Repository: TurboRx/chronos-randbats
- Engine: Tensor-native JAX battle simulator with hardware-accelerated rollouts
- Framework: JAX / Flax / Optax
Architecture
- Model: Non-Causal Transformer Encoder (~9.65M parameters)
- Embedding Dimension (
d_model): 256 - Multi-Head Attention: 8 heads
- Encoder Depth: 6 layers
- Feed-Forward Dimension (
d_ff): 1024 - Heads:
- Policy Head (9 actions: 4 moves, 5 switches)
- Value Head (Scalar valuation in [-1, 1])
- Opponent Prediction Head (Simultaneous action prediction)
Checkpoints
checkpoint_latest.pkl: Model parameters trained via PPO self-play and benchmark sparring.metrics.json: Latest training metrics, update count, and turn counters.
Configuration
- Model type
- chronos_transformer
Identity and Version
- Repository
- TurboRx/Chronoquazicalar
- Publisher
- Turbo
- Task
- Reinforcement learning
- Modality
- Control
- Library
- Not stated by the source
- Parameters
- Not stated by the source
- Languages
- jax, ppo
- Revision
- d7d83d08bffcbf17679708d6237a3195e0000ce4
- First published
- 2026-09-21
- Last updated
- 2026-10-09
Files and Weights
7 files, 71.9 MB in total. The weights are 1 file totalling 16.2 KB in npz.
Weights1 file · 16.2 KB
Configuration2 files · 460 B
Documentation1 file · 1.1 KB
Other2 files · 71.9 MB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| mechanics_tables.npz | Weights | 16.2 KB | 05a7285a3be6 |
| config.json | Configuration | 179 B | — |
| metrics.json | Configuration | 281 B | — |
| README.md | Documentation | 1.1 KB | — |
| bc_checkpoint_latest.pkl | Other | 33.3 MB | 3d5e9b126186 |
| checkpoint_latest.pkl | Other | 38.6 MB | 2da0a335b935 |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
- Download size
- 16.2 KB
Download from Turbo
Released by Turbo through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 16.2 KB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About Chronoquazicalar
Can I use Chronoquazicalar commercially?
Yes. Chronoquazicalar is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
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