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
- insideBIGDATA Guide to Data Platforms for Artificial Intelligence and Deep Learning on November 13, 2018 at 10:37 am
With AI and DL, storage is cornerstone to handling the deluge of data constantly generated in today’s hyperconnected world. It is a vehicle that captures and shares data to create business value. In t... […]
- Deep Learning Chipset Market Will Reach Revenue of US$1,264.78 Mn by the End of 2025 on November 13, 2018 at 12:54 am
Global Deep Learning Chipset market report provides analysis for the period 2015 – 2025, wherein the period from 2017 to 2025 is the forecast and 2016 is the base year. The data for 2015 has been incl... […]
- Global Deep Learning System Market Insights, Forecast 2018-2025: By Product, Application, Manufacturer, Sales and Segmentation - Global QYResearch on November 12, 2018 at 10:39 pm
The report is readily available and can be dispatched within 4hr after payment confirmation. Buy Now This Report From Here: globalqyresearch.com/checkout-form/0/537931 Global QYResearch is the one spo... […]
- BOXX Demos New Deep Learning Workstation and More at SC18 on November 12, 2018 at 10:32 am
News and research before you hear about it on CNBC and others. Claim your 2-week free trial to StreetInsider Premium here. AUSTIN, TEXAS, Nov. 12, 2018 (GLOBE NEWSWIRE) -- BOXX Technologies, the leadi... […]
- Top 10 Machine Learning, Deep Learning, and Data Science Courses for Beginners (Python and R) on November 12, 2018 at 10:32 am
Did you know that 50- 80% of your enterprise business processes can be automated with AssistEdge? Identify processes, deploy bots and scale effortlessly with AssistEdge. Data Science, Machine ... […]
- Sophos Intercept X with Deep Learning Honored with 2018 CRN® Tech Innovator Award on November 12, 2018 at 6:00 am
BURLINGTON, Mass., Nov. 12, 2018 (GLOBE NEWSWIRE) -- Sophos (LSE: SOPH), a global leader in network and endpoint security, announced today that CRN, ® a brand of The Channel Company, has ... […]
- How NLP, ML and Deep Learning Can Transform Your CX Strategy on November 11, 2018 at 5:26 am
The post aims to give the reader a gentle overview of NLP, ML and Deep Learning and make the connection of how it can be applied in the context of customer experience and support. Machine learning Ima... […]
- Bayesian Deep Learning for Exoplanet Atmospheric Retrieval on November 8, 2018 at 7:33 pm
Over the past decade, the study of exoplanets has shifted from their detection to the characterization of their atmospheres. Atmospheric retrieval, the inverse modeling technique used to determine an ... […]
- How Deep Learning can solve the problem of global climate change on November 7, 2018 at 7:51 am
With the help of a use case, Dr Patrick Vetter, Head of Competence Center Data Science at Supper and Supper GMBH explains how a deep learning methodology can locate and segment wind turbines on satell... […]
- Deep Learning for Medical Imaging Fares Poorly on External Data on November 7, 2018 at 7:07 am
November 07, 2018 - A deep learning model trained to identify pneumonia on a contained sample of medical images was unable to achieve the same level of accuracy when let loose on data from external he... […]
via Google News and Bing News