Collect all scripts into one place

Author: silvery107

README.md

@@ -74,8 +74,8 @@ A pretrained policy (`rainbow_v7`) ships in `checkpoints/`, so this step is opti
 your own, use the vendored `legged_gym`:
 
 ```bash
-python src/legged_gym/scripts/train.py --task hopper          # runs saved to logs/hopper/
-python src/legged_gym/scripts/play.py --task hopper           # visualize the latest run
+python scripts/train_policy.py --task hopper   # runs saved to logs/hopper/
+python scripts/play_policy.py --task hopper    # visualize the latest run
 ```
 
 Training runs are written to `logs/hopper/<timestamp>_<run_name>/`. The codesign config loads

src/legged_gym/scripts/play.py scripts/play_policy.pysrc/legged_gym/scripts/play.py renamed to scripts/play_policy.py

@@ -0,0 +1,18 @@
+import numpy as np
+import os
+from datetime import datetime
+
+import isaacgym
+from legged_gym.envs import *
+from legged_gym.utils import get_args, task_registry, set_seed
+import torch
+torch.set_float32_matmul_precision('high')
+
+def train(args):
+    env, env_cfg = task_registry.make_env(name=args.task, args=args)
+    ppo_runner, train_cfg = task_registry.make_alg_runner(env=env, name=args.task, args=args)
+    ppo_runner.learn(num_learning_iterations=train_cfg.runner.max_iterations, init_at_random_ep_len=True)
+
+if __name__ == '__main__':
+    args = get_args()
+    train(args)