Researchers at the U.S. Army Research Laboratory and the University of Texas at Austin have developed new techniques for robots or computer programs to learn how to perform tasks by interacting with a human instructor.
The findings of the study will be presented and published at the Association for the Advancement of Artificial Intelligence Conference in New Orleans, Louisiana, Feb. 2-7.
ARL and UT researchers considered a specific case where a human provides real-time feedback in the form of critique. First introduced by collaborator Dr. Peter Stone, a professor at the University of Texas at Austin, along with his former doctoral student, Brad Knox, as TAMER, or Training an Agent Manually via Evaluative Reinforcement, the ARL/UT team developed a new algorithm called Deep TAMER.
It is an extension of TAMER that uses deep learning – a class of machine learning algorithms that are loosely inspired by the brain to provide a robot the ability to learn how to perform tasks by viewing video streams in a short amount of time with a human trainer.
According to Army researcher Dr. Garrett Warnell, the team considered situations where a human teaches an agent how to behave by observing it and providing critique, for example, “good job” or “bad job” -similar to the way a person might train a dog to do a trick. Warnell said the researchers extended earlier work in this field to enable this type of training for robots or computer programs that currently see the world through images, which is an important first step in designing learning agents that can operate in the real world.
Many current techniques in artificial intelligence require robots to interact with their environment for extended periods of time to learn how to optimally perform a task. During this process, the agent might perform actions that may not only be wrong, like a robot running into a wall for example, but catastrophic like a robot running off the side of a cliff. Warnell said help from humans will speed things up for the agents, and help them avoid potential pitfalls.
As a first step, the researchers demonstrated Deep TAMER’s success by using it with 15 minutes of human-provided feedback to train an agent to perform better than humans on the Atari game of bowling – a task that has proven difficult for even state-of-the-art methods in artificial intelligence. Deep-TAMER-trained agents exhibited superhuman performance, besting both their amateur trainers and, on average, an expert human Atari player.
Within the next one to two years, researchers are interested in exploring the applicability of their newest technique in a wider variety of environments: for example, video games other than Atari Bowling and additional simulation environments to better represent the types of agents and environments found when fielding robots in the real world.
Their work will be published in the AAAI 2018 conference proceedings.
“The Army of the future will consist of Soldiers and autonomous teammates working side-by-side,” Warnell said. “While both humans and autonomous agents can be trained in advance, the team will inevitably be asked to perform tasks, for example, search and rescue or surveillance, in new environments they have not seen before. In these situations, humans are remarkably good at generalizing their training, but current artificially-intelligent agents are not.”
Deep TAMER is the first step in a line of research its researchers envision will enable more successful human-autonomy teams in the Army. Ultimately, they want autonomous agents that can quickly and safely learn from their human teammates in a wide variety of styles such as demonstration, natural language instruction and critique.
The Latest on: Deep Learning
via Google News
The Latest on: Deep Learning
- Deep Learning Helps U Missouri Researchers Predict New Material Behaviors on April 18, 2019 at 3:42 am
Graphene is a big, almost-magical subject in material science. This 2D layer of carbon elements one atom thick isn't just the thinnest and lightest material known on earth, but it's also ... […]
- Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization on April 18, 2019 at 2:10 am
Automated diagnosis of tuberculosis (TB) from chest X-Rays (CXR) has been tackled with either hand-crafted algorithms or machine learning approaches such as support vector machines (SVMs) and ... […]
- You can still Pay What You Want for this AI and Deep Learning Bundle on April 17, 2019 at 10:46 am
This is a time-limited offer, ending soon. In some cases, such as with Online Courses, a store credit refund within 15 days of purchase is possible if you are unhappy with it; this does not apply to ... […]
- Deep Learning Market 2019: Company Profiles, Emerging Technologies, Global Segments, Landscape and Demand by Forecast to 2023 on April 17, 2019 at 9:49 am
Apr 17, 2019 (AB Digital via COMTEX) -- Deep Learning Market Highlights: As per the latest report published by Market Research Future (MRFR), the global deep learning market is expected to post a ... […]
- New deep-learning approach predicts protein structure from amino acid sequence on April 17, 2019 at 8:05 am
Nearly every fundamental biological process necessary for life is carried out by proteins. They create and maintain the shapes of cells and tissues; constitute the enzymes that catalyze life ... […]
- Bringing Deep Learning for Geospatial Applications to Life on April 17, 2019 at 12:38 am
Whenever we start to talk about artificial intelligence, machine learning, or deep learning, the cautionary tales from science fiction cinema arise: HAL 9000 from 2001: A Space Odyssey, the T-series ... […]
- Researchers Attempt To Predict & Prevent Suicide Using Deep Learning And Math on April 16, 2019 at 5:05 pm
For years scientists have focused on the causes behind veteran suicide. Now the U.S. Department of Energy (DOE) and several national labs are teaming up to use deep learning and mathematics to ... […]
- Why You Should Consider Google AI Platform For Your Machine Learning Projects on April 16, 2019 at 7:28 am
It is the founder of TensorFlow, the most popular framework for building sophisticated machine learning and deep learning models. It also had Cloud ML Engine, a platform for training and deploying ML ... […]
- Supercharge Your Sales Efforts With Deep Learning on April 11, 2019 at 7:05 am
Deep learning, one of the most effective approaches to artificial intelligence, continues to gain traction in the business world. And AI is booming: Salesforce’s “2018 State of Sales ... […]
via Bing News