record_video.py now functions, needs tuning

agents/dqn_v2/record_video.py

@@ -5,7 +5,7 @@ from stable_baselines3.common.vec_env import VecVideoRecorder, DummyVecEnv, VecE
 
 env_id = "ALE/Pacman-v5"
 video_folder = "videos/"
-video_length = 100 #steps
+video_length = 1000 #steps
 
 vec_env = DummyVecEnv([lambda: gym.make(env_id, render_mode="rgb_array")])
 model = DQN.load("ALE-Pacman-v5")
@@ -16,8 +16,10 @@ model = DQN.load("ALE-Pacman-v5")
 
 # vec_env = Monitor(gym.make(env_id, render_mode="rgb_array"))
 
+print("\n\n\n")
+print(vec_env)
+print("\n\n\n")
 
-# print(vec_env)
 obs = vec_env.reset()
 
 
@@ -26,10 +28,11 @@ vec_env = VecVideoRecorder(vec_env,
                            video_folder, 
                            record_video_trigger=lambda x: x == 0,
                            video_length=video_length,
-                           name_prefix=f"video-{env_id}")
+                          #  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()
+obs = vec_env.reset()
 print(vec_env)
 for _ in range(video_length + 1):
   action, states = model.predict(obs)

agents/dqn_v2/videos/rl-video-step-0-to-step-1000.meta.json

@@ -0,0 +1 @@
+{"step_id": 0, "content_type": "video/mp4"}