train

---

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

/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

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

Cartpole-Specific Defaults

For cartpole, these settings are known to work well with the current defaults (--lr 3e-4 --max-grad-norm 0.5 --lam 0.95 --gamma 0.99):

• --minibatch-size 256
• --std-dev 0.15 --learn-std --entropy-coeff 0.01
• --max-traj-len 500 --n-itr 500 --num-procs 12
• --no-mirror (cartpole has no body symmetry)

Suggest these defaults when the user trains cartpole, but let them override.