Modified the record_video.py file, still not functional.

agents/dqn_v2/record_video.py

@@ -1,22 +1,38 @@
 import gymnasium as gym
-from stable_baselines3.common.vec_env import VecVideoRecorder, DummyVecEnv
+from stable_baselines3 import DQN
+from stable_baselines3.common.monitor import Monitor
+from stable_baselines3.common.vec_env import VecVideoRecorder, DummyVecEnv, VecEnv
 
 env_id = "ALE/Pacman-v5"
-video_folder = "./"
+video_folder = "videos/"
 video_length = 100 #steps
 
 vec_env = DummyVecEnv([lambda: gym.make(env_id, render_mode="rgb_array")])
+model = DQN.load("ALE-Pacman-v5")
+# output: <stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv object at 0x0000029974DC6550>
 
+# vec_env = gym.make(env_id, render_mode="rgb_array")
+# output <OrderEnforcing<PassiveEnvChecker<AtariEnv<ALE/Pacman-v5>>>>
+
+# vec_env = Monitor(gym.make(env_id, render_mode="rgb_array"))
+
+
+# print(vec_env)
 obs = vec_env.reset()
 
-# Record the video starting at the first step
-vec_env = VecVideoRecorder(vec_env, video_folder,
-                       record_video_trigger=lambda x: x == 0, video_length=video_length,
-                       name_prefix=f"{env_id}")
 
+# Record the video starting at the first step
+vec_env = VecVideoRecorder(vec_env,
+                           video_folder, 
+                           record_video_trigger=lambda x: x == 0,
+                           video_length=video_length,
+                           name_prefix=f"video-{env_id}")
+#  Once I make the environment, now I need to walk through it...???
+#   I want to act according to the policy that has been trained
 vec_env.reset()
+print(vec_env)
 for _ in range(video_length + 1):
-  action = [vec_env.action_space.sample()]
+  action, states = model.predict(obs)
   obs, _, _, _ = vec_env.step(action)
-# Save the video
+# # Save the video
 vec_env.close()