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NASA NTRS1998

Preliminary Work for Examining the Scalability of Reinforcement Learning

Researchers began studying automated agents that learn to perform multiple-step tasks early in the history of artificial intelligence (Samuel, 1963; Samuel, 1967; Waterman, 1970; Fikes, Hart & Nilsonn, 1972). Multiple-step tasks are tasks that can only be solved via a sequence of decisions, such as control problems, robotics problems, classic problem-solving, and game-playing. The objective of agents attempting to learn such tasks is to use the resources they have available in order to become more proficient at the tasks. In particular, each agent attempts to develop a good policy, a mapping from states to actions, that allows it to select actions that optimize a measure of its performance on the task; for example, reducing the number of steps necessary to complete the task successfully. Our study focuses on reinforcement learning, a set of learning techniques where the learner performs trial-and-error experiments in the task and adapts its policy based on the outcome of those experiments. Much of the work in reinforcement learning has focused on a particular, simple representation, where every problem state is represented explicitly in a table, and associated with each state are the actions that can be chosen in that state. A major advantage of this table lookup representation is that one can prove that certain reinforcement learning techniques will develop an optimal policy for the current task. The drawback is that the representation limits the application of reinforcement learning to multiple-step tasks with relatively small state-spaces. There has been a little theoretical work that proves that convergence to optimal solutions can be obtained when using generalization structures, but the structures are quite simple. The theory says little about complex structures, such as multi-layer, feedforward artificial neural networks (Rumelhart & McClelland, 1986), but empirical results indicate that the use of reinforcement learning with such structures is promising. These empirical results make no theoretical claims, nor compare the policies produced to optimal policies. A goal of our work is to be able to make the comparison between an optimal policy and one stored in an artificial neural network. A difficulty of performing such a study is finding a multiple-step task that is small enough that one can find an optimal policy using table lookup, yet large enough that, for practical purposes, an artificial neural network is really required. We have identified a limited form of the game OTHELLO as satisfying these requirements. The work we report here is in the very preliminary stages of research, but this paper provides background for the problem being studied and a description of our initial approach to examining the problem. In the remainder of this paper, we first describe reinforcement learning in more detail. Next, we present the game OTHELLO. Finally we argue that a restricted form of the game meets the requirements of our study, and describe our preliminary approach to finding an optimal solution to the problem.

Clouse, Jeff↗

NASA NTRSDate not supplied

Acquisition of and Access to Research Omics Data

Omics data are essential for understanding the myriad and complex effects of space environments on humans. To assure maximum benefit from these kinds of data, the NASA Human Research Program Data Management Plan stipulates that human omics data should be archived within and accessed through the NASA Life Sciences Portal (NLSP). The NLSP has the capability to acquire and provision access to omics (and other kinds of) research results for individual and ad-hoc groups of subjects at the direction of institutional review boards, or other authorizing bodies or individuals, per institutional, program and investigation-specific policies and procedures. However, because some single-subject omics data, like CT scans and other kinds of large, complex biomedical data, could be used to identify heretofore unknown risks to the subject’s health, or, in certain cases, be used to identify a subject, NASA Policy Directive 7170.1 describes various policies regarding the management of and access to “research genetic testing” data, which includes many kinds of omics data. For example, NPD 7170.1 prohibits access to human research genetic data by NASA personnel who make employment decisions for the subjects from whom the data were obtained. To meet the objective of acquiring research omics data for NLSP in compliance with the policies in NPD 7170.1 and other applicable NASA policies, we designed NOMADS (the NLSP Omics Multimodal Acquisition of Data System), a new component that supports the transfer of large research data files, including research genetic testing data, using one of several different transfer mechanisms. The choice of mechanism is made by the submitter of the data, with guiding information from the system, and is likely to often be determined in large part by the nature and source location of the data. For example, for small files where the source data files are not already stored in a cloud storage system, users are likely to prefer to transfer their data to the NLSP via a web browser. Conversely, for large sets of files already organized and stored in a cloud storage system, users may opt for NOMAD’s cloud-to-cloud transfer method. All omics datasets targeted for the NASA Life Sciences Data Archive must pass a variety of quality checks to ensure data integrity and adherence to the standards defined by the LSDA Data Submission Guidelines (DSG) (see https://nlsp.nasa.gov/explore/lsdahome/datasubmit). These include requirements that data are consistent with open standards established by the omics community. Non-compliant data will not be accepted however archivists are available to advise submitters on how to revise data submissions and re-submit until compliance is achieved. Following compliance with the LSDA DSG, omics data next undergo a variety of additional quality checks to ensure the data meet omics community standards. Domain specific Omics data quality control tools and techniques are continually evolving and linked to the advancements in omics assays utilized and thus, the tools and techniques utilized by the LSDA for data quality control and validation will need to be sustained accordingly. All human omics data will be access controlled according to the policies described above, and requiring IRB approval for any additional access grants once the data are acquired (including access for analysis using the NLSP workspace tools).

Omics↗

NASA NTRS2024

Acquisition of and Access to Research Omics Data

Omics↗

NASA NTRS2018

Stabilized Approach Criteria: Bridging the Gap Between Theory and Practice

Approach and landing is the most common phase of flight for aviation accidents, accounting annually for approximately 65 percent of all accidents. A Flight Safety Foundation study of 16 years of runway excursions determined that 83 percent could have been avoided with a decision to go around. In other words, 54 percent of all accidents could potentially be prevented by going around. A critical industry policy designed to help prevent such accidents is the go-around policy. However, the collective industry performance of complying with go-around policies is extremely poor and only about three percent of unstable approaches result in a go-around. Improving the go-around compliance rate holds tremendous potential in reducing approach and landing accidents. There are many reasons for flight crews ignoring go-around policies related to pilot judgement and company policies. Examples are the collective industry norm to accept the noncompliance of go-around policies, management being disengaged from go-around noncompliance, and pilot fatigue and lack of situational awareness. One of the biggest factors is that pilots see current stabilized-approach criteria as too complex and restrictive for the operational environment. Following the American Airlines 1420 accident (Little Rock, 1999), where the aircraft overran the runway upon landing and crashed, the National Transportation Safety Board (NTSB) recommended that the Federal Aviation Administration (FAA) define detailed parameters for a stabilized approach, and develop detailed criteria indicating when a go-around should be performed. The experiment discussed in this presentation is the first step towards developing these go-around criteria for commercial transport aircraft.

pilot performance↗

NASA NTRSDate not supplied

NASA’s Planetary Protection Program to Assure Mission Safety and Success

NASA’s planetary protection program seeks to understand and control harmful contamination of solar systems targets of exploration by terrestrial contamination and prevent harmful biological contamination of the Earth-Moon system by extraterrestrial life, should it exist. To accomplish these objective’s NASA has developed a balanced safety and mission assurance strategy that leverages COSPAR Policy guidelines, workshops, scientiﬁc consensus, partnerships and international working groups to develop policy and implementation guidelines. Upcoming crewed missions to the Moon and Mars, as well as robotic missions to small solar system bodies, Europa, Titan and Mars are some of the driving activities of astrobiological interest that continue to emphasize the importance of planetary protection throughout the project life cycle. Development of a responsive and updated agency planetary protection policy has been a focus area in supporting upcoming mission opportunities for exploration to include Mars sample return and crewed mission concepts. An extensive update of this policy is underway which encompasses crewed and robotic procedural polices, a general technical requirements standard, and an implementation handbook. During this timeframe NASA has been working with the international community to develop scientiﬁc consensus, and to identify and ﬁll in knowledge gaps for developing balanced policy guidelines, incorporation of risk informed decision making and quantitative technical standards. NASA has developed a planetary protection roadmap as a technology management strategy to track and monitor the development of each knowledge gaps. The Committee of Space Research (COSPAR) Policy on Planetary Protection and the National Academies of Science, Engineering, and Medicine’s Committee of Planetary Protection are used to inform updates to the planetary protection polices and guidelines. This integrated strategy for planetary protection seeks to provide a transparent, structured approach for enabling missions, providing guidance for NASA and NASA partnered missions, and being responsive to the increased interest and activities in space exploration whilst maintaining an understanding and control of harmful contamination.

James Benardini↗

NASA NTRSDate not supplied

NASA's Planetary Protection Program to Assure Mission Safety and Success

NASA's planetary protection program seeks to understand and control harmful contamination of solar systems targets of exploration by terrestrial contamination and prevent harmful biological contamination of the Earth-Moon system by extraterrestrial life, should it exist. To accomplish these objective's NASA has developed a balanced safety and mission assurance strategy that leverages COSPAR Policy guidelines, workshops, scientific consensus, partnerships and international working groups to develop policy and implementation guidelines. Upcoming crewed missions to the Moon and Mars, as well as robotic missions to small solar system bodies, Europa, Titan and Mars are some of the driving activities of astrobiological interest that continue to emphasize the importance of planetary protection throughout the project life cycle.Development of a responsive and updated agency planetary protection policy has been a focus area in supporting upcoming mission opportunities for exploration to include Mars sample return and crewed mission concepts. An extensive update of this policy is underway which encompasses crewed and robotic procedural polices, a general technical requirements standard, and an implementation handbook. During this timeframe NASA has been working with the international community to develop scientific consensus, and to identify and fill in knowledge gaps for developing balanced policy guidelines, incorporation of risk informed decision making and quantitative technical standards. NASA has developed a planetary protection roadmap as a technology management strategy to track and monitor the development of each knowledge gaps. The Committee of Space Research (COSPAR) Policy on Planetary Protection and the National Academies of Science, Engineering, and Medicine's Committee of Planetary Protection are used to inform updates to the planetary protection polices and guidelines. This integrated strategy for planetary protection seeks to provide a transparent, structured approach for enabling missions, providing guidance for NASA and NASA partnered missions, and being responsive to the increased interest and activities in space exploration whilst maintaining an understanding and control of harmful contamination.

Planetary Protection↗

NASA NTRS2016

Climate and Health Impacts of US Emissions Reductions Consistent with 2 C

An emissions trajectory for the US consistent with 2 C warming would require marked societal changes, making it crucial to understand the associated benefits. Previous studies have examined technological potentials and implementation costs and public health benefits have been quantified for less-aggressive potential emissions-reduction policies, but researchers have not yet fully explored the multiple benefits of reductions consistent with 2 C. We examine the impacts of such highly ambitious scenarios for clean energy and vehicles. US transportation emissions reductions avoid approx.0.03 C global warming in 2030 (0.15 C in 2100), whereas energy emissions reductions avoid approx.0.05-0.07 C 2030 warming (approx.0.25 C in 2100). Nationally, however, clean energy policies produce climate disbenefits including warmer summers (although these would be eliminated by the remote effects of similar policies if they were undertaken elsewhere). The policies also greatly reduce damaging ambient particulate matter and ozone. By 2030, clean energy policies could prevent approx.175,000 premature deaths, with approx.22,000 (11,000-96,000; 95% confidence) fewer annually thereafter, whereas clean transportation could prevent approx.120,000 premature deaths and approx.14,000 (9,000-52,000) annually thereafter. Near-term national benefits are valued at approx.US$250 billion (140 billion to 1,050billion) per year, which is likely to exceed implementation costs. Including longer-term, worldwide climate impacts, benefits roughly quintuple, becoming approx.5-10 times larger than estimated implementation costs. Achieving the benefits, however, would require both larger and broader emissions reductions than those in current legislation or regulations.

policies↗

NASA NTRS1986

Statistical methodologies for the control of dynamic remapping

Following an initial mapping of a problem onto a multiprocessor machine or computer network, system performance often deteriorates with time. In order to maintain high performance, it may be necessary to remap the problem. The decision to remap must take into account measurements of performance deterioration, the cost of remapping, and the estimated benefits achieved by remapping. We examine the tradeoff between the costs and the benefits of remapping two qualitatively different kinds of problems. One problem assumes that performance deteriorates gradually, the other assumes that performance deteriorates suddenly. We consider a variety of policies for governing when to remap. In order to evaluate these policies, statistical models of problem behaviors are developed. Simulation results are presented which compare simple policies with computationally expensive optimal decision policies; these results demonstrate that for each problem type, the proposed simple policies are effective and robust.

Saltz, J. H.↗

NASA NTRS1993

NASA safety program activities in support of the Space Exploration Initiatives Nuclear Propulsion program

The activities of the joint NASA/DOE/DOD Nuclear Propulsion Program Technical Panels have been used as the basis for the current development of safety policies and requirements for the Space Exploration Initiatives (SEI) Nuclear Propulsion Technology development program. The Safety Division of the NASA Office of Safety and Mission Quality has initiated efforts to develop policies for the safe use of nuclear propulsion in space through involvement in the joint agency Nuclear Safety Policy Working Group (NSPWG), encouraged expansion of the initial policy development into proposed programmatic requirements, and suggested further expansion into the overall risk assessment and risk management process for the NASA Exploration Program. Similar efforts are underway within the Department of Energy to ensure the safe development and testing of nuclear propulsion systems on Earth. This paper describes the NASA safety policy related to requirements for the design of systems that may operate where Earth re-entry is a possibility. The expected plan of action is to support and oversee activities related to the technology development of nuclear propulsion in space, and support the overall safety and risk management program being developed for the NASA Exploration Program.

Sawyer, J. C., Jr.↗

NASA NTRS2006

Robust Coordination for Large Sets of Simple Rovers

The ability to coordinate sets of rovers in an unknown environment is critical to the long-term success of many of NASA;s exploration missions. Such coordination policies must have the ability to adapt in unmodeled or partially modeled domains and must be robust against environmental noise and rover failures. In addition such coordination policies must accommodate a large number of rovers, without excessive and burdensome hand-tuning. In this paper we present a distributed coordination method that addresses these issues in the domain of controlling a set of simple rovers. The application of these methods allows reliable and efficient robotic exploration in dangerous, dynamic, and previously unexplored domains. Most control policies for space missions are directly programmed by engineers or created through the use of planning tools, and are appropriate for single rover missions or missions requiring the coordination of a small number of rovers. Such methods typically require significant amounts of domain knowledge, and are difficult to scale to large numbers of rovers. The method described in this article aims to address cases where a large number of rovers need to coordinate to solve a complex time dependent problem in a noisy environment. In this approach, each rover decomposes a global utility, representing the overall goal of the system, into rover-specific utilities that properly assign credit to the rover s actions. Each rover then has the responsibility to create a control policy that maximizes its own rover-specific utility. We show a method of creating rover-utilities that are "aligned" with the global utility, such that when the rovers maximize their own utility, they also maximize the global utility. In addition we show that our method creates rover-utilities that allow the rovers to create their control policies quickly and reliably. Our distributed learning method allows large sets rovers be used unmodeled domains, while providing robustness against rover failures and changing environments. In experimental simulations we show that our method scales well with large numbers of rovers in addition to being robust against noisy sensor inputs and noisy servo control. The results show that our method is able to scale to large numbers of rovers and achieves up to 400% performance improvement over standard machine learning methods.

Tumer, Kagan↗

NASA NTRSDate not supplied

Exploring Transfers Between Earth-Moon Halo Orbits via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization, a multi-objective deep reinforcement learning algorithm, is used to examine the design space of low-thrust trajectories for a SmallSat transferring between two libration point orbits in the Earth-Moon system. Using Multi-Reward Proximal Policy Optimization, multiple policies are simultaneously and efficiently trained on three distinct trajectory design scenarios. Each policy is trained to create a unique control scheme based on the trajectory design scenario and assigned reward function: a unique combination of weights scaling competing objectives that guide the spacecraft to the target mission orbit, incentivize faster flight times, and penalize propellant mass usage. Then, the policies are evaluated on the same set of perturbed initial conditions in each scenario to generate the propellant mass usages, flight times, and state discontinuities from a reference trajectory for each control scheme. This solution space of low-thrust trajectories for a SmallSat is used to examine the multi-objective trade space for the trajectory design scenario. By autonomously constructing the solution space, insights into the required propellant mass, flight time, and transfer geometry are rapidly achieved.

Christopher J Sullivan↗

NASA NTRSDate not supplied

Exploring the Low-Thrust Transfer Design Space in an Ephemeris Model via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization (MRPPO) is a multi-objective reinforcement learning algorithm used to train multiple policies to uncover solutions within a multi-objective solution space. MRPPO is used in this paper to train policies to construct low-thrust transfers for a SmallSat from the vicinity of !2 to an !5 short period orbit in the Sun-Earth-Moon system. First, the policies are trained in this scenario in the circular restricted three-body problem. This information is used to initialize the policies before training in a higher-fidelity ephemeris model; a process known as transfer learning. The recovered segments of the solution space will be compared to fundamental dynamical structures to both examine the results of MRPPO in this complex design scenario and explore the effectiveness of transfer learning.

Christopher J Sullivan↗

NASA NTRSDate not supplied

Exploring the Low-Thrust Transfer Design Space in an Ephemeris Model via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization (MRPPO) is a multi-objective reinforcement learning algorithm used to train multiple policies to uncover solutions within a multi-objective solution space. MRPPO is used in this paper to train policies to construct low-thrust transfers for a SmallSat from the vicinity of L2 to an L5 short period orbit in the Sun-Earth-Moon system. First, the policies are trained in this scenario in the circular restricted three-body problem. This information is used to initialize the policies before training in a higher-fidelity ephemeris model; a process known as transfer learning. The recovered segments of the solution space will be compared to fundamental dynamical structures to both examine the results of MRPPO in this complex design scenario and explore the effectiveness of transfer learning.

Christopher J. Sullivan↗

NASA NTRS2021

Exploring Transfers Between Earth-Moon Halo Orbits via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization, a multi-objective deep reinforcement learning algorithm, is used to examine the design space of low-thrust trajectories for a SmallSat transferring between two libration point orbits in the Earth- Moon system. Using Multi-Reward Proximal Policy Optimiza- tion, multiple policies are simultaneously and efficiently trained on three distinct trajectory design scenarios. Each policy is trained to create a unique control scheme based on the trajectory design scenario and assigned reward function: a unique combination of weights scaling competing objectives that guide the spacecraft to the target mission orbit, incentivize faster flight times, and penalize propellant mass usage. Then, the policies are evaluated on the same set of perturbed initial conditions in each scenario to generate the propellant mass usages, flight times, and state discontinuities from a reference trajectory for each control scheme. This solution space of low-thrust trajectories for a SmallSat is used to examine the multi-objective trade space for the trajectory design scenario. By autonomously constructing the solution space, insights into the required propellant mass, flight time, and transfer geometry are rapidly achieved.

Mashiku, Alinda K.↗

NASA NTRSDate not supplied

NASA Nuclear Flight Safety – New Protocols for Safety and Mission Assurance

Over the past 4 years, the United States (U.S.) Government has issued several new National policies that fundamentally change the approach to nuclear flight safety for aerospace applications, including the complete revision of the Federal policy for handling launch of spacecraft containing space nuclear systems. In response, the National Aeronautics and Space Administration (NASA) is updating its nuclear flight safety program while still maintaining consistency with other Federal policies, international conventions, and NASA’s own policies. The recently-completed revision of the NASA Procedural Requirements (NPR) document for nuclear flight safety is a key step in the evolution of NASA policy and practice in this area. This paper will briefly describe the more significant changes in the new NASA NPR.

Donald Helton↗

NASA NTRSDate not supplied

NASA Planetary Protection Program Overview and Update

NASA has an integrated planetary protection strategy that leverages COSPAR Policy guide-lines, workshops, scientific consensus, partnerships and international working groups to develop policy and implementation guidelines. The Oÿce of Safety and Mission Assurance, which in-cludes NASA’s Oÿce of Planetary Protection, depends on these activities to support NASA’s missions in the assurance of crew safety and mission success while protecting the public and planetary environment from harmful contamination. This report will detail the overall planetary protection policy development perspective to sup-port NASA’s upcoming mission opportunities for exploration to include crewed Mars and sam-ple return missions. NASA continues to evolve its strategies, policies, and standards. NASA’s support in the COSPAR workshop series on Refining Planetary Protection Requirements for Crewed Missions to Mars have resulted in a NASA interim directive and paved the way for the development of NASA planetary protection roadmaps. These Agency level roadmaps address technology development and testing required to fill knowledge gaps for developing responsive policy guidelines and quantitative technical standards. In addition to workshops and tech-nology management strategies, NASA is also leveraging the National Academies of Science, Engineering, and Medicine’s Committee of Planetary Protection to serve as a source of advice on planetary protection measures. Finally, the report will include how the Oÿce of Safety and Mission Assurance is incorporating planetary protection into existing and new multi-agency partnerships.

Frank Groen↗

NASA NTRS2022

Exploring the Low-Thrust Transfer Design Space in an Ephemeris Model via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization (MRPPO) is a multi-objective reinforcement learning algorithm used to train multiple policies to uncover solutions within a multi-objective solution space. MRPPO is used in this paper to train policies to construct low-thrust transfers for a SmallSat from the vicinity of 𝐿2 to an 𝐿5 short period orbit in the Sun-Earth-Moon system. First, the policies are trained in this scenario in the circular restricted three-body problem. This information is used to initialize the policies before training in a higher-fidelity ephemeris model; a process known as transfer learning. The recovered segments of the solution space will be compared to fundamental dynamical structures to both examine the results of MRPPO in this complex design scenario and explore the effectiveness of transfer learning.

Mashiku, Alinda K.↗

NASA NTRS2012

Dynamic Communication Resource Negotiations

Today's advanced network management systems can automate many aspects of the tactical networking operations within a military domain. However, automation of joint and coalition tactical networking across multiple domains remains challenging. Due to potentially conflicting goals and priorities, human agreement is often required before implementation into the network operations. This is further complicated by incompatible network management systems and security policies, rendering it difficult to implement automatic network management, thus requiring manual human intervention to the communication protocols used at various network routers and endpoints. This process of manual human intervention is tedious, error-prone, and slow. In order to facilitate a better solution, we are pursuing a technology which makes network management automated, reliable, and fast. Automating the negotiation of the common network communication parameters between different parties is the subject of this paper. We present the technology that enables inter-force dynamic communication resource negotiations to enable ad-hoc inter-operation in the field between force domains, without pre-planning. It also will enable a dynamic response to changing conditions within the area of operations. Our solution enables the rapid blending of intra-domain policies so that the forces involved are able to inter-operate effectively without overwhelming each other's networks with in-appropriate or un-warranted traffic. It will evaluate the policy rules and configuration data for each of the domains, then generate a compatible inter-domain policy and configuration that will update the gateway systems between the two domains.

policy negotiation↗