dineshj1

DEPRECATED. See https://www.seas.upenn.edu/~dineshj/pubs.bib

@article{watson2026let,
  title     = {Let it Cook: Learning to Wait in Sequential Decision Making},
  author    = {Watson, Christopher and Krishna, Arjun and Jayaraman, Dinesh and Alur, Rajeev},
  journal = {Reinforcement Learning Conference (RLC)},
  year      = {2026},
  url       = {https://openreview.net/forum?id=eA56bCxlFT},
  abstract  = {In sequential decision making, an agent typically observes its environment and acts at every timestep. However, such active participation may not always be necessary; tasks such as brewing coffee include periods that are served equally well by letting the environment evolve without constant monitoring and control. During such periods, the agent could simply wait to conserve its resources, or redirect its attention to another task. We capitalize on these opportunities by training a "waiting policy" that decides where and how long to wait. This involves forgoing sensing to commit to a wait action, representin

dineshj1

# First make sure you pip install google-api-python-client
from apiclient.discovery import build
from httplib2 import Http
from oauth2client import file, client, tools

def store_opts(opt):  # opt is the object returned by argparse
    params = dict(opt.__dict__)
    
   # here I remove parameters I don't want recorded in google sheets
    params.pop('retain_in_test', None)

dineshj1

# Written with help from http://warmspringwinds.github.io/tensorflow/tf-slim
# /2016/12/21/tfrecords-guide/
import os

import ipdb
import numpy as np
import tensorflow as tf
from tqdm import tqdm

tf_dir = './'

dineshj1

# Dinesh Jayaraman

# Based on code by
# Authors: Fabian Pedregosa <fabian.pedregosa@inria.fr>
#          Olivier Grisel <olivier.grisel@ensta.org>
#          Mathieu Blondel <mathieu@mblondel.org>
#          Gael Varoquaux
# License: BSD 3 clause (C) INRIA 2011

print(__doc__)

dineshj1

%demo_textprogressbar
%This a demo for textprogressbar script
textprogressbar('calculating outputs: ');
for i=1:100,
    textprogressbar(i);
    pause(0.1);
end
textprogressbar('done');

dineshj1

import argparse
import time

start_time=time.time();
################## Argument Parsing #####################################
parser=argparse.ArgumentParser();
parser.add_argument('-s','--solver', default='', type=str); # if empty, solver is created, else read
parser.add_argument('-res', '--resume_from', default='', type=str); #if not empty, resumes training from given file
parser.add_argument('-ft', '--finetune_from', default='', type=str);

dineshj1

function submitjobs(nosubmit, numjobs, main)
  try
    xlwrite_path='../data_utils/xlwrite/';
    addpath(xlwrite_path);
    javaaddpath([xlwrite_path 'poi_library/poi-3.8-20120326.jar']);
    javaaddpath([xlwrite_path 'poi_library/poi-ooxml-3.8-20120326.jar']);
    javaaddpath([xlwrite_path 'poi_library/poi-ooxml-schemas-3.8-20120326.jar']);
    javaaddpath([xlwrite_path 'poi_library/xmlbeans-2.3.0.jar']);
    javaaddpath([xlwrite_path 'poi_library/dom4j-1.6.1.jar']);
    javaaddpath([xlwrite_path 'poi_library/stax-api-1.0.1.jar']);

dineshj1

from tsne import bh_sne
import numpy as np
from skimage.transform import resize

from matplotlib import pyplot as plt

def gray_to_color(img):
    if len(img.shape) == 2:
        img = np.dstack((img, img, img))
    return img

dineshj1

  // generate [0..n-1]
  auto seq = [](size_t n) -> std::vector<size_t> {
      std::vector<size_t> v(n);
      for (size_t i=0; i<n; ++i) v[i] = i;
      return v;
  };
  auto index = seq(n);

  // n * n distance matrix
  std::vector<D> dists(n * n);