plot improvement is a mess, but getting there

plot_improvement.py

@@ -1,5 +1,6 @@
 import argparse
 from numpy import load, ndarray
+import os
 
 parser = argparse.ArgumentParser()
 parser.add_argument("-f", "--filepath", required=True, help="Specify the file path to the agent.", type=str)
@@ -8,10 +9,7 @@ args = parser.parse_args()
 filepath = args.filepath
 npdata = load(filepath)
 
-print(type(npdata['results']))
 evaluations = ndarray.tolist(npdata['results'])
-print(type(evaluations))
-print(len(evaluations))
 # print(evaluations)
 sorted_evals = []
 for eval in evaluations:
@@ -39,4 +37,43 @@ print("num evals: " + str(len(mean_eval_rewards)))
 # The number of elements is going to vary for each training run
 # The number of evaluation episodes will be constant, 10.
 # I need to convert to a regular list first
-# I could iterate over each element
+# I could iterate over each element
+
+agent_dirs = []
+for d in os.listdir("agents/"):
+    if "dqn_v2" in d:
+        agent_dirs.append(d)
+# Now I have a list of dirs with the evals.
+# Iterate over the dirs, append the file path, load the evals, calculate the average score of the eval, then return a list with averages
+eval_list = []
+for d in agent_dirs:
+    path = "agents/" + d + "/evaluations.npz"
+    evals = ndarray.tolist(load(path)["results"])
+    eval_list.append(evals)
+# for i in eval_list:
+#     print(i)
+#     print()
+
+def remove_outliers(evals: list) -> list:
+    trimmed = []
+    for eval in evals:
+        eval.sort()
+        eval.pop(0)
+        eval.pop()
+        trimmed.append(eval)
+    return trimmed
+
+avgs = [[]]
+index = 0
+for i in eval_list:
+    avgs.append(i)
+    for j in i:
+        j.sort()
+        j.pop()
+        j.pop(0)
+        avgs[index].append(sum(j) / len(j))
+    index += 1
+    
+print(avgs)
+
+