Agent Skills › rohanpsingh/LearningHumanoidWalking › train

train

GitHub

用于启动机器人环境的PPO训练任务，解析参数生成命令并后台执行。支持Cartpole、H1等环境及自定义超参数，提供进度监控与日志路径反馈。

.claude/skills/train/SKILL.md rohanpsingh/LearningHumanoidWalking

Trigger Scenarios

启动机器人训练运行PPO算法配置训练超参数

Install

npx skills add rohanpsingh/LearningHumanoidWalking --skill train -g -y

More Options

Non-standard path

npx skills add https://github.com/rohanpsingh/LearningHumanoidWalking/tree/main/.claude/skills/train -g -y

Use without installing

npx skills use rohanpsingh/LearningHumanoidWalking@train

指定 Agent (Claude Code)

npx skills add rohanpsingh/LearningHumanoidWalking --skill train -a claude-code -g -y

安装 repo 全部 skill

npx skills add rohanpsingh/LearningHumanoidWalking --all -g -y

预览 repo 内 skill

npx skills add rohanpsingh/LearningHumanoidWalking --list

SKILL.md

Frontmatter

{
    "name": "train",
    "description": "Launch a training run for a robot environment using PPO",
    "allowed-tools": "Bash, Read, Glob",
    "argument-hint": [
        "env-name"
    ],
    "disable-model-invocation": true
}

/train — Launch a PPO Training Run

Parse the user's request from $ARGUMENTS and construct a training command.

Command Template

RAY_ADDRESS= uv run python run_experiment.py train --env <ENV> --logdir <LOGDIR> [OPTIONS...]

Available Environments

Name	Description
`cartpole`	Cartpole swing-up (simplest, good for testing)
`h1`	Unitree H1 standing task
`jvrc_walk`	JVRC humanoid basic walking
`jvrc_step`	JVRC humanoid stepping with planned footsteps

Hyperparameters (defaults)

Flag	Default	Description
`--n-itr`	20000	Training iterations
`--lr`	1e-4	Learning rate
`--gamma`	0.99	Discount factor
`--std-dev`	0.223	Action noise
`--learn-std`	off	Learn action noise (flag)
`--entropy-coeff`	0.0	Entropy regularization
`--clip`	0.2	PPO clipping
`--minibatch-size`	64	Minibatch size
`--epochs`	3	Optimization epochs per update
`--num-procs`	12	Parallel workers
`--num-envs-per-worker`	1	Vectorized envs per worker
`--max-grad-norm`	0.05	Gradient clipping
`--max-traj-len`	400	Episode horizon
`--eval-freq`	100	Eval every N iterations
`--seed`	None	Random seed
`--device`	auto	Training device (auto/cpu/cuda)
`--no-mirror`	off	Disable symmetry wrapper (flag)
`--recurrent`	off	Use LSTM policy (flag)
`--continued`	None	Path to pretrained weights

Instructions

Determine the environment name from the user's request. If ambiguous, ask.
Use --logdir /tmp/training_runs unless the user specifies a different path.
Only include flags that differ from defaults — keep the command clean.
Show the user the full command you're about to run.
Run the command in the background using run_in_background: true on the Bash tool. Set a generous timeout (600000ms).
After launching, tell the user the logdir path and how to check progress (you can tail the output using the task ID).
If the user asks to check on training, use TaskOutput with block: false to check the latest output.