import os import sys import pandas as pd import numpy as np from results_plot.plotter import get_crm_results, get_ltl_results, get_qrm_results, save_file # To get results on normal # python compute_mean_std.py False 0 # To get results on missing # python compute_mean_std.py True 0 # To get results on noise # python compute_mean_std.py False 0.1 missing = eval(sys.argv[1]) noise = float(sys.argv[2]) if not missing and noise <= 0: env_type = "normal" elif missing: env_type = "missing" elif noise > 0: env_type = "noise" type2name = {"sr": "Success Rate", "reward": "Average Return"} alg_names = ["crm", "hrm", "qrm", "ppo", "a2c", "ddpg", "dqn", "ddqn"] # alg_names = ["hrm"] envs = ["office", "taxi", "water", "cheetah"] for alg_name in alg_names: for plot_type in ["sr", "reward"]: for env_name in envs: try: # env_name: str, missing: bool, noise: float, if alg_name == "crm": results = get_crm_results(env_name, missing, noise, algs=["crm-rs"]) save_file(results, plot_type, env_name, env_type, "rm", alg_name="crm") # hrm result shared by get crm results function elif alg_name == "hrm": results = get_crm_results(env_name, missing, noise, algs=["hrm-rs"]) save_file(results, plot_type, env_name, env_type, "rm", alg_name="hrm") # get qrm results come from different format elif alg_name == "qrm": results = get_qrm_results(env_name, missing, noise, plot_type=plot_type, algs=["qrm-rs"]) save_file(results, plot_type, env_name, env_type, "rm", alg_name="qrm") else: reward_types = ["progress_adrs", "hybrid_adrs", "naive"] for r in reward_types: results = get_ltl_results(env_name, missing, noise, reward_types=[r], alg_name=alg_name) save_file(results, plot_type, env_name, reward_type=r, env_type=env_type, alg_name=alg_name) except Exception as e: print(alg_name + " does not work") print(e.args) pass