JackM323
c5ca79a354
Training environment to make a walk model for the hexapod generated code that will be checked
60 lines
1.7 KiB
Markdown
60 lines
1.7 KiB
Markdown
JackBot ML training and evaluation
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Quick-start
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1. Create a Python virtualenv and activate it:
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```bash
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python -m venv .venv
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source .venv/bin/activate
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```
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2. Install dependencies (GPU users should install `torch` appropriate for their CUDA):
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```bash
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pip install -r requirements.txt
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```
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3. Quick training (short, for smoke test):
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```bash
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python ml/run_train.py --timesteps 50000 --model ml/checkpoints/ppo_joint_command
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```
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Use multi-robot training with `--num-robots` and `--robot-spacing`:
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```bash
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python ml/run_train.py --timesteps 50000 --model ml/checkpoints/ppo_joint_command --num-robots 3 --robot-spacing 0.75
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```
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Choose the start pose at reset with `--start-pose`:
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```bash
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python ml/run_train.py --timesteps 50000 --model ml/checkpoints/ppo_joint_command --start-pose init_deg
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```
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If you want to watch the agent train in the PyBullet window, add `--gui`:
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```bash
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python ml/run_train.py --timesteps 50000 --model ml/checkpoints/ppo_joint_command --gui
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```
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4. Evaluate the trained model in GUI mode:
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```bash
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python ml/run_eval.py --model ml/checkpoints/ppo_joint_command.zip --episodes 3 --gui
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```
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For evaluation with multiple robots and start pose:
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```bash
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python ml/run_eval.py --model ml/checkpoints/ppo_joint_command.zip --episodes 3 --gui --num-robots 2 --robot-spacing 0.6 --start-pose init_deg
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```
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Notes
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- `ml/env.py` exposes a `JackBotEnv` gym environment that uses your `JackBotUrdf.urdf`.
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- The observation is `[joint_angles..., vx, vy, vz, omega]` and the action is per-joint delta in [-1,1].
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- Start with small timesteps and in `use_gui=False` to speed up iteration. Increase timesteps and tune the reward for better walking quality.
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- Keep the trained models off hardware until they behave well in simulation.
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