causal-gpt-rl 0.8.0

Inference runtime for Causal-GPT-RL policy bundles

---

license: other license_name: polyform-noncommercial-1.0.0 license_link: https://polyformproject.org/licenses/noncommercial/1.0.0 library_name: pytorch tags:

• reinforcement-learning
• gymnasium
• mujoco
• causal-gpt-rl

---

Causal GPT-RL

GPT-style transformers (GPT-2, Llama) running as RL policies in continuous-control environments.

Both LLM generation and RL interaction are autoregressive:

token           → next token                           (LLM generation)
(state, action) → (next state from env, next action)   (RL rollout)

Causal GPT-RL policies act stably under their own rollouts — long-horizon control without the drift that has historically kept transformers from being usable as RL agents.

A single autoregressive model drives full-episode rollouts via KV cache — no critic, no auxiliary networks at inference.

This repository is the public inference runtime. It loads policy bundles, runs Gymnasium/MuJoCo rollouts, and provides small evaluation helpers.

• Code (GitHub): ccnets-team/causal-gpt-rl
• Run logs (W&B, public): wandb.ai/junhopark/Causal GPT-RL
• Hugging Face org: https://huggingface.co/ccnets
• Website: https://ccnets.org
• LinkedIn: https://www.linkedin.com/company/ccnets

Released under PolyForm Noncommercial 1.0.0. For commercial licensing, contact the maintainers via ccnets.org.

Product Overview

Causal GPT-RL is a GPT-based reinforcement learning product that turns offline trajectory data into deployable decision-making agents.

The system is designed for users who have recorded interaction data, simulation logs, or control trajectories and want to train policies that can act in sequential decision-making environments.

At the public package level, causal-gpt-rl provides the inference runtime for loading and evaluating trained policy bundles. These bundles can be executed in Gymnasium / MuJoCo environments and used to reproduce rollout behavior, benchmark performance, and demonstrate GPT-style reinforcement learning agents.

For commercial use, Causal GPT-RL is intended to support custom training from private offline datasets, cloud-based training workflows, and deployment of trained policy bundles through managed infrastructure.

In short:

• Public PyPI package: provides the inference runtime for loading Hugging Face or local policy bundles
• Hugging Face Hub: provides public pretrained policy bundles for testing, evaluation, and demos
• Commercial product: trains custom GPT-style RL agents from user-provided offline datasets
• Future direction: managed cloud training and SaaS-based decision-agent deployment

Causal GPT-RL is positioned as a bridge between offline reinforcement learning research and deployable AI agents for real-world sequential decision-making.

Install

For Hub loading and MuJoCo environments:

pip install "causal-gpt-rl[hub,mujoco]"

For local development:

git clone https://github.com/ccnets-team/causal-gpt-rl.git
cd causal-gpt-rl
python -m pip install -e ".[hub,mujoco]"

For private bundles, authenticate first:

hf auth login

Quick Start

import gymnasium as gym

from causal_gpt_rl.inference import load_runner_from_hub, run_episodes

env = gym.make("Ant-v5")
runner = load_runner_from_hub(
    repo_id="ccnets/causal-gpt-rl",
    subfolder="ant-v5",
)

stats = run_episodes(env, runner, num_episodes=5, seed=0)
env.close()
print(stats["return_mean"], stats["return_std"])

Notebook version: examples/hub_quickstart.ipynb

Supported Environments

Env	Bundle	Ctx	Return	Norm.	Medium Ref.
Ant-v5	ant-v5	32	3339.51±1115.40	50.56±16.54	86.54
HalfCheetah-v5	halfcheetah-v5	32	6865.15±2657.69	43.17±16.11	74.83
Hopper-v5	hopper-v5	32	2836.28±987.67	73.40±25.72	72.91
Walker2d-v5	walker2d-v5	32	3883.30±684.09	56.69±9.99	83.26
Humanoid-v5	humanoid-v5	32	6511.87±2855.54	75.38±33.62	81.30

Training data is expert-free: bundles are trained using Minari simple and medium datasets only; expert trajectories are not used for training.

Return and Norm. are mean±std over 50 episodes with seeds 0..49. Ctx is context length. max_steps=1000, and KV cache max length is capped to Ctx.

Normalized scores use random=0 and expert=100:

100 * (return - random_ref) / (expert_ref - random_ref)

Medium reference scores are shown for context and are not the normalization baseline.

Evaluation runtime:

causal-gpt-rl 0.2.1
torch 2.12.0+cu132
gymnasium 1.2.2
mujoco 3.8.1
minari 0.5.3

Bundle Format

All public bundles include:

bundle/
  model.safetensors
  config.json
  state_normalizer.safetensors

• model.safetensors — model state dict for inference.
• config.json — model config, observation specs, action specs, context length, and optional env_id.
• state_normalizer.safetensors — state normalization statistics used by the policy.

Hugging Face Layout

Recommended layout:

ccnets/causal-gpt-rl/
  ant-v5/
    model.safetensors
    config.json
    state_normalizer.safetensors
    README.md

For local bundles, use load_runner("path/to/bundle").

API

from causal_gpt_rl.inference import (
    PolicyRunner,                          # step-wise rollout policy with KV cache
    load_runner,                           # load runner from a local bundle directory
    load_runner_from_hub,                  # load runner from a Hugging Face Hub repo
    run_episodes,                          # evaluate over N episodes; returns stats dict
    export_bundle,                         # write a bundle directory from a runner
    convert_legacy_bundle_to_safetensors,  # migrate legacy bundles to the safetensors format
)

Development Checks

python -m compileall -q causal_gpt_rl
python -m unittest discover -s tests
python -m build
python -m twine check dist/*

License

Released under PolyForm Noncommercial License 1.0.0. See LICENSE for details. For commercial licensing, contact the maintainers via ccnets.org.