update save method in pipeline

src/tensorneat/common/stateful_class.py

@@ -33,11 +33,16 @@ class StatefulBaseClass:
 
         return state
 
-    def show_config(self):
+    def show_config(self, registered_objects=None):
+        if registered_objects is None:  # root call
+            registered_objects = []
+
         config = {}
         for key, value in self.__dict__.items():
-            if isinstance(value, StatefulBaseClass):
-                config[str(key)] = value.show_config()
+            if isinstance(value, StatefulBaseClass) and value not in registered_objects:
+                registered_objects.append(value)
+                config[str(key)] = value.show_config(registered_objects)
+
             else:
                 config[str(key)] = str(value)
         return config

src/tensorneat/problem/rl/brax.py

@@ -1,7 +1,7 @@
 import jax.numpy as jnp
 from brax import envs
 
-from .rl_jit import RLEnv
+from .rl_jit import RLEnv, norm_obs
 
 
 class BraxEnv(RLEnv):
@@ -37,13 +37,17 @@ class BraxEnv(RLEnv):
             save_path=None,
             height=480,
             width=480,
+            output_type="rgb_array",
             *args,
             **kwargs,
         ):
     
+        assert output_type in ["rgb_array", "gif"]
+
         import jax
         import imageio
         from brax.io import image
+        import numpy as np
 
         obs, env_state = self.reset(randkey)
         reward, done = 0.0, False
@@ -52,13 +56,16 @@ class BraxEnv(RLEnv):
         def step(key, env_state, obs):
             key, _ = jax.random.split(key)
 
+            if self.obs_normalization:
+                obs = norm_obs(state, obs)
+
             if self.action_policy is not None:
                 forward_func = lambda obs: act_func(state, params, obs)
                 action = self.action_policy(key, forward_func, obs)
             else:
                 action = act_func(state, params, obs)
 
-            obs, env_state, r, done, _ = self.step(randkey, env_state, action)
+            obs, env_state, r, done, info = self.step(randkey, env_state, action)
             return key, env_state, obs, r, done
 
         jit_step = jax.jit(step)
@@ -70,14 +77,19 @@ class BraxEnv(RLEnv):
             if done:
                 break
         
+        print("Total reward: ", reward)
+
         imgs = image.render_array(
-            sys=self.env.sys, trajectory=state_histories, height=height, width=width
+            sys=self.env.sys, trajectory=state_histories, height=height, width=width, camera="track"
         )
 
+        if output_type == "rgb_array":
+            imgs = np.array(imgs)
+            return imgs
+
         if save_path is None:
             save_path = f"{self.env_name}.gif"
 
         imageio.mimsave(save_path, imgs, *args, **kwargs)
 
         print("Gif saved to: ", save_path)
-        print("Total reward: ", reward)