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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)
args = parser.parse_args()
filepath = args.filepath
npdata = load(filepath)
evaluations = ndarray.tolist(npdata['results'])
# print(evaluations)
sorted_evals = []
for eval in evaluations:
sorted_evals.append(sorted(eval))
# Now I have a sorted list.
# Now just pop the first and last elements of each eval
for eval in sorted_evals:
eval.pop(0)
eval.pop()
print()
# print(sorted_evals)
# Now that I have my sorted evaluations, I can calculate the mean episode reward for each eval
mean_eval_rewards = []
for eval in sorted_evals:
mean_eval_rewards.append(sum(eval) / len(eval))
# Now I should have a list with the mean evaluation reward with the highest and lowest score tossed out
print(mean_eval_rewards)
print("num evals: " + str(len(mean_eval_rewards)))
# I'm dealing with a 2D array. Each element contains an array of ten data points
# 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
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)
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