Add common, refactor eval with eval_policy

lerobot/common/envs/factory.py

@@ -0,0 +1,42 @@
+from torchrl.envs.transforms import StepCounter, TransformedEnv
+
+from lerobot.common.envs.simxarm import SimxarmEnv
+
+
+def make_env(cfg):
+    assert cfg.env == "simxarm"
+    env = SimxarmEnv(
+        task=cfg.task,
+        from_pixels=cfg.from_pixels,
+        pixels_only=cfg.pixels_only,
+        image_size=cfg.image_size,
+    )
+
+    # limit rollout to max_steps
+    env = TransformedEnv(env, StepCounter(max_steps=cfg.episode_length))
+
+    return env
+
+
+# def make_env(env_name, frame_skip, device, is_test=False):
+#     env = GymEnv(
+#         env_name,
+#         frame_skip=frame_skip,
+#         from_pixels=True,
+#         pixels_only=False,
+#         device=device,
+#     )
+#     env = TransformedEnv(env)
+#     env.append_transform(NoopResetEnv(noops=30, random=True))
+#     if not is_test:
+#         env.append_transform(EndOfLifeTransform())
+#         env.append_transform(RewardClipping(-1, 1))
+#     env.append_transform(ToTensorImage())
+#     env.append_transform(GrayScale())
+#     env.append_transform(Resize(84, 84))
+#     env.append_transform(CatFrames(N=4, dim=-3))
+#     env.append_transform(RewardSum())
+#     env.append_transform(StepCounter(max_steps=4500))
+#     env.append_transform(DoubleToFloat())
+#     env.append_transform(VecNorm(in_keys=["pixels"]))
+#     return env