Readme Updated

ml/README.md

@@ -1,59 +0,0 @@
-JackBot ML training and evaluation
-
-Quick-start
-
-1. Create a Python virtualenv and activate it:
-
-```bash
-python -m venv .venv
-source .venv/bin/activate
-```
-
-2. Install dependencies (GPU users should install `torch` appropriate for their CUDA):
-
-```bash
-pip install -r requirements.txt
-```
-
-3. Quick training (short, for smoke test):
-
-```bash
-python ml/run_train.py --timesteps 50000 --model ml/checkpoints/ppo_joint_command
-```
-
-Use multi-robot training with `--num-robots` and `--robot-spacing`:
-
-```bash
-python ml/run_train.py --timesteps 50000 --model ml/checkpoints/ppo_joint_command --num-robots 3 --robot-spacing 0.75
-```
-
-Choose the start pose at reset with `--start-pose`:
-
-```bash
-python ml/run_train.py --timesteps 50000 --model ml/checkpoints/ppo_joint_command --start-pose init_deg
-```
-
-If you want to watch the agent train in the PyBullet window, add `--gui`:
-
-```bash
-python ml/run_train.py --timesteps 50000 --model ml/checkpoints/ppo_joint_command --gui
-```
-
-4. Evaluate the trained model in GUI mode:
-
-```bash
-python ml/run_eval.py --model ml/checkpoints/ppo_joint_command.zip --episodes 3 --gui
-```
-
-For evaluation with multiple robots and start pose:
-
-```bash
-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
-```
-
-Notes
-
-- `ml/env.py` exposes a `JackBotEnv` gym environment that uses your `JackBotUrdf.urdf`.
-- The observation is `[joint_angles..., vx, vy, vz, omega]` and the action is per-joint delta in [-1,1].
-- Start with small timesteps and in `use_gui=False` to speed up iteration. Increase timesteps and tune the reward for better walking quality.
-- Keep the trained models off hardware until they behave well in simulation.