Added option flags to the watch_agent.py file.

agents/version_2/evals/evaluations_v3.npz

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agents/version_2/version_2-3/events.out.tfevents.1715694677.5da7e309893b.24.0

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agents/version_2/watch_agent.py

@@ -5,23 +5,44 @@ import gymnasium as gym
 
 import argparse
 
+MODEL_NAME = "ALE-Pacman-v5"
+loaded_model = DQN.load(MODEL_NAME)
+
 # This script should have some options
 # 1. Turn off the stochasticity as determined by the ALEv5
 #   Even if deterministic is set to true in evaluate policy, the environment will ignore this 25% of the time
 #   To compensate for this, we can set the repeat action probability to 0
+#       DONE
+# 2. Print out the evaluation metrics or save to file
+# 3. Render in the ALE or not 
+#       DONE
+# 4. Print the keyword args for the environment? I think this might be helpful...
+#       IN PROGRESS
+# 5. 
 
 parser = argparse.ArgumentParser()
 parser.add_argument("-r", "--repeat_action_probability", help="repeat action probability", type=float, default=0.25)
+parser.add_argument("-f", "--frameskip", help="frameskip", type=int, default=4)
+parser.add_argument("-o", "--observe", help="observe agent", action="store_const", const=True)
+parser.add_argument("-p", "--print", help="print environment information", action="store_const", const=True)
 args = parser.parse_args()
 
-MODEL_NAME = "ALE-Pacman-v5"
-rpt_act_prob = args.repeat_action_probability
-
-loaded_model = DQN.load(MODEL_NAME)
+# Toggle the render mode based on the -o flag
+if args.observe == True:
+    mode = "human"
+else:
+    mode = "rgb_array"
 
 # Retrieve the environment
-eval_env = Monitor(gym.make("ALE/Pacman-v5", render_mode="rgb_array", repeat_action_probability=rpt_act_prob))
+eval_env = Monitor(gym.make("ALE/Pacman-v5", 
+                            render_mode=mode, 
+                            repeat_action_probability=args.repeat_action_probability,
+                            frameskip=args.frameskip,))
 
+if args.print == True:
+    env_info = str(eval_env.spec).split(", ")
+    for item in env_info:
+        print(item)
 # Evaluate the policy
 mean_rwd, std_rwd = evaluate_policy(loaded_model.policy, eval_env, n_eval_episodes=1)
 print("mean rwd: ", mean_rwd)