chore: release v0.1.0

(cherry picked from commit 82525f882f3924a332d9ce40bf64255d0d14f6a4)

scripts/test_all_demos.py

@@ -0,0 +1,158 @@
+# Copyright (C) 2020-2025 Motphys Technology Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""
+Run all training demos with both JAX and PyTorch backends.
+
+This script iterates through all available demo environments and runs
+training for each one using both training backends.
+"""
+
+import argparse
+import subprocess
+import sys
+from typing import List
+
+# List of all available demo environments
+ALL_DEMOS = [
+    "cartpole",
+    "dm-cheetah",
+    "dm-hopper-stand",
+    "dm-hopper-hop",
+    "dm-reacher",
+    "dm-stander",
+    "dm-walker",
+    "dm-runner",
+    "bounce_ball",
+    "go1-flat-terrain-walk",
+    "go1-rough-terrain-walk",
+    "go1-stairs-terrain-walk",
+    "anymal_c_navigation_flat",
+    "franka-lift-cube",
+    "franka-open-cabinet",
+]
+
+# Available training backends
+BACKENDS = ["jax", "torch"]
+
+
+def run_command(env: str, backend: str, extra_args: List[str]) -> int:
+    """Run a single training command."""
+    cmd = [
+        "uv",
+        "run",
+        "scripts/train.py",
+        "--env",
+        env,
+        "--train-backend",
+        backend,
+    ] + extra_args
+
+    print(f"\n{'=' * 80}")
+    print(f"Running: {' '.join(cmd)}")
+    print(f"{'=' * 80}\n")
+
+    result = subprocess.run(cmd)
+
+    if result.returncode != 0:
+        print(f"\n❌ Error: Training failed for {env} with {backend} backend")
+        return result.returncode
+
+    print(f"\n✅ Success: Training completed for {env} with {backend} backend")
+    return 0
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Run training demos with both JAX and PyTorch backends")
+    parser.add_argument(
+        "--demos",
+        nargs="+",
+        choices=ALL_DEMOS + ["all"],
+        default=["all"],
+        help="List of demos to run (default: all)",
+    )
+    parser.add_argument(
+        "--backends",
+        nargs="+",
+        choices=BACKENDS + ["all"],
+        default=["all"],
+        help="Training backends to use (default: all)",
+    )
+    parser.add_argument(
+        "--extra-args",
+        nargs=argparse.REMAINDER,
+        default=[],
+        help="Extra arguments to pass to train.py",
+    )
+    parser.add_argument(
+        "--stop-on-error",
+        action="store_true",
+        help="Stop execution if any training fails",
+    )
+
+    args = parser.parse_args()
+
+    # Determine which demos to run
+    if "all" in args.demos:
+        demos_to_run = ALL_DEMOS
+    else:
+        demos_to_run = args.demos
+
+    # Determine which backends to use
+    if "all" in args.backends:
+        backends_to_run = BACKENDS
+    else:
+        backends_to_run = args.backends
+
+    print(f"Will run {len(demos_to_run)} demos with {len(backends_to_run)} backend(s)")
+    print(f"Demos: {demos_to_run}")
+    print(f"Backends: {backends_to_run}")
+    print(f"Total training runs: {len(demos_to_run) * len(backends_to_run)}")
+
+    failed_runs = []
+
+    for demo in demos_to_run:
+        for backend in backends_to_run:
+            returncode = run_command(demo, backend, args.extra_args)
+
+            if returncode != 0:
+                failed_runs.append((demo, backend))
+                if args.stop_on_error:
+                    print("\n⚠️  Stopping due to error as requested")
+                    sys.exit(1)
+
+    # Summary
+    print(f"\n{'=' * 80}")
+    print("SUMMARY")
+    print(f"{'=' * 80}")
+    total_runs = len(demos_to_run) * len(backends_to_run)
+    successful_runs = total_runs - len(failed_runs)
+
+    print(f"Total runs: {total_runs}")
+    print(f"Successful: {successful_runs}")
+    print(f"Failed: {len(failed_runs)}")
+
+    if failed_runs:
+        print("\nFailed runs:")
+        for demo, backend in failed_runs:
+            print(f"  - {demo} ({backend})")
+        sys.exit(1)
+    else:
+        print("\n✅ All training runs completed successfully!")
+        sys.exit(0)
+
+
+if __name__ == "__main__":
+    main()

scripts/view.py

@@ -37,9 +37,13 @@ class NpEnvRunner:
         action_space = self._env.action_space
         if isinstance(action_space, gym.spaces.Box):
             size = (self._env.num_envs, *action_space.shape)
+            low = action_space.low
+            high = action_space.high
+            low = np.where(np.isneginf(low), -1e6, low)
+            high = np.where(np.isposinf(high), 1e6, high)
             return np.random.uniform(
-                low=action_space.low,
-                high=action_space.high,
+                low=low,
+                high=high,
                 size=size,
             ).astype(action_space.dtype)
         else: