def load_trials(trial_dir):
directory = jup_dir + trial_dir
ws_list = []
model_list = []
min_length = float('inf')
for entry in os.scandir(directory):
model, env, args, ws = load_workspace(entry.path)
if len(ws["raw_rew_hist"]) < min_length:
min_length = len(ws["raw_rew_hist"])
ws_list.append(ws)
model_list.append(model)
min_length = int(min_length)
rewards = np.zeros((min_length, len(ws_list)))
for i, ws in enumerate(ws_list):
rewards[:, i] = np.array(ws["raw_rew_hist"][:min_length])
return ws_list, model_list, rewards
ep_acts = torch.stack(acts_list).reshape(-1, act_size)
ep_rews = torch.stack(rews_list).reshape(-1, 1)
return ep_obs1, ep_acts, ep_rews, None, ep_obs1
#%%
jup_dir = "/home/sgillen/work/"
trial_dir = "ssac/switched_rl/data_needle/50k_slow_longer"
directory = jup_dir + trial_dir
ws_list = []
model_list = []
min_length = float('inf')
for entry in os.scandir(directory):
model, env, args, ws = load_workspace(entry.path)
if len(ws["raw_rew_hist"]) < min_length:
min_length = len(ws["raw_rew_hist"])
ws_list.append(ws)
model_list.append(model)
min_length = int(min_length)
rewards = np.zeros((min_length, len(ws_list)))
for i, ws in enumerate(ws_list):
rewards[:, i] = np.array(ws["raw_rew_hist"][:min_length])
print("seagul", rewards[-1, :].mean(), rewards[-1, :].std())
fig, ax = smooth_bounded_curve(rewards)
ssac_size = rewards.shape[0]
import matplotlib.pyplot as plt
import numpy as np
from numpy import pi
from stable_baselines import TD3
from stable_baselines.results_plotter import load_results
import torch.utils.data
from torch.multiprocessing import Pool
from itertools import product
import os
import matplotlib
script_path = os.path.realpath(__file__).split("/")[:-1]
script_path = "/".join(script_path) + "/"
print(script_path)
model, env, args, ws = load_workspace(
script_path + "data_needle/50k_slow_longer/trial3738150792--3-11_18-0")
env_name = ws['env_name']
config = ws['env_config']
def load_trials(trial_dir):
directory = script_path + trial_dir
ws_list = []
model_list = []
min_length = float('inf')
for entry in os.scandir(directory):
model, env, args, ws = load_workspace(entry.path)
if len(ws["raw_rew_hist"]) < min_length:
min_length = len(ws["raw_rew_hist"])