Moved the config and watch scripts to the root directory. Split the watch script into two scripts: watch and evaluate.

agents/dqn_v2-6/evals.txt

@@ -0,0 +1,6 @@
+-----
+Evaluation of agents/dqn_v2-6/ALE-Pacman-v5 on 16 May 2024 at 11:24:26 AM
+Episodes evaluated: 1
+mean_rwd: 399.0
+std_rwd: 0.0
+

evaluate_agent.py

@@ -0,0 +1,73 @@
+from stable_baselines3 import DQN
+from stable_baselines3.common.evaluation import evaluate_policy
+from stable_baselines3.common.monitor import Monitor
+import gymnasium as gym
+
+import argparse
+from datetime import datetime
+
+
+# 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
+#       DONE
+# 4. Print the keyword args for the environment? I think this might be helpful...
+#       DONE (ish), printing the environment specifications.
+# 5. Add option flag to accept file path for model
+#       DONE
+# 6. Add option flag to accept number of episodes
+#       DONE
+# 7. Save evaluations in a log file
+#       DONE
+# 8. Add option flag for mean rewards/length or discrete rewards/lengths
+#       IN PROGRESS
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-r", "--repeat_action_probability", help="repeat action probability, default 0.25", type=float, default=0.25)
+parser.add_argument("-f", "--frameskip", help="frameskip, default 4", 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)
+parser.add_argument("-e", "--num_episodes", help="specify the number of episodes to evaluate, default 1", type=int, default=1)
+parser.add_argument("-a", "--agent_filepath", help="file path to agent to watch, minus the .zip extension", type=str, required=True)
+# parser.add_argument("-s", "--savefile", help="Specify a filepath to save the evaluation metrics.", type=str, default="evals")
+args = parser.parse_args()
+
+model_name = args.agent_filepath
+model = DQN.load(model_name)
+# There should really be a condition here to catch input defining directories with forward slashes
+dirs = model_name.split("/")
+# remove the last item, as it is the zip file
+dirs.pop()
+model_dir = "/".join(dirs)
+print(type(model_dir))
+print(model_dir)
+
+# Retrieve the environment
+eval_env = Monitor(gym.make("ALE/Pacman-v5", 
+                            render_mode="rgb_array", 
+                            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
+# Toggle the mean or discrete evaluations here
+mean_rwd, std_rwd = evaluate_policy(model.policy, eval_env, n_eval_episodes=args.num_episodes)
+
+# savefile = args.savefile
+savefile = model_dir + "/evals"
+date = datetime.now().strftime("%d %b %Y")
+time = datetime.now().strftime("%I:%M:%S %p")
+
+with open(f"{savefile}.txt", "a") as file:
+    file.write("-----\n")
+    file.write(f"Evaluation of {model_name} on {date} at {time}\n")
+    file.write(f"Episodes evaluated: {args.num_episodes}\n")
+    file.write(f"mean_rwd: {mean_rwd}\n")    
+    file.write(f"std_rwd: {std_rwd}\n\n")
+    

agents/watch_agent.py → watch_agent.py

@@ -24,7 +24,7 @@ import argparse
 parser = argparse.ArgumentParser()
 parser.add_argument("-r", "--repeat_action_probability", help="repeat action probability, default 0.25", type=float, default=0.25)
 parser.add_argument("-f", "--frameskip", help="frameskip, default 4", type=int, default=4)
-parser.add_argument("-o", "--observe", help="observe agent", action="store_const", const=True)
+# 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)
 parser.add_argument("-e", "--num_episodes", help="specify the number of episodes to evaluate, default 1", type=int, default=1)
 parser.add_argument("-a", "--agent_filepath", help="file path to agent to watch, minus the .zip extension", type=str, required=True)
@@ -34,14 +34,14 @@ MODEL_NAME = args.agent_filepath
 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"
+# if args.observe == True:
+#     mode = "human"
+# else:
+#     mode = "rgb_array"
 
 # Retrieve the environment
 eval_env = Monitor(gym.make("ALE/Pacman-v5", 
-                            render_mode=mode, 
+                            render_mode="human", 
                             repeat_action_probability=args.repeat_action_probability,
                             frameskip=args.frameskip,))
 
@@ -50,7 +50,9 @@ if args.print == True:
     for item in env_info:
         print(item)
 # Evaluate the policy
-mean_rwd, std_rwd = evaluate_policy(loaded_model.policy, eval_env, n_eval_episodes=args.num_episodes)
-print("eval episodes: ", args.num_episodes)
-print("mean rwd: ", mean_rwd)
-print("std rwd: ", std_rwd)
+# mean_rwd, std_rwd = 
+
+evaluate_policy(loaded_model.policy, eval_env, n_eval_episodes=args.num_episodes)
+# print("eval episodes: ", args.num_episodes)
+# print("mean rwd: ", mean_rwd)
+# print("std rwd: ", std_rwd)