Adaptive computer‐generated forces for simulator‐based training, Expert Systems with Applications

Simulator-based training is in constant pursuit of increasing level of realism. The transition from doctrine-driven computer-generated forces (CGF) to adaptive CGF represents one such effort. The use of doctrine-driven CGF is fraught with challenges such as modeling of complex expert knowledge and adapting to the trainees’ progress in real time. Therefore, this paper reports on how the use of adaptive CGF can overcome these challenges. Using a self-organizing neural network to implement the adaptive CGF, air combat maneuvering strategies are learned incrementally and generalized in real time. The state space and action space are extracted from the same hierarchical doctrine used by the rule-based CGF. In addition, this hierarchical doctrine is used to bootstrap the self-organizing neural network to improve learning efficiency and reduce model complexity. Two case studies are conducted. The first case study shows how adaptive CGF can converge to the effective air combat maneuvers against rule-based CGF. The subsequent case study replaces the rule-based CGF with human pilots as the opponent to the adaptive CGF. The results from these two case studies show how positive outcome from learning against rule-based CGF can differ markedly from learning against human subjects for the same tasks. With a better understanding of the existing constraints, an adaptive CGF that performs well against rule-based CGF and human subjects can be designed

Advanced Message Routing for Scalable Distributed Simulations

The Joint Forces Command (JFCOM) Experimentation Directorate (J9)'s recent Joint Urban Operations (JUO) experiments have demonstrated the viability of Forces Modeling and Simulation in a distributed environment. The JSAF application suite, combined with the RTI-s communications system, provides the ability to run distributed simulations with sites located across the United States, from Norfolk, Virginia to Maui, Hawaii. Interest-aware routers are essential for communications in the large, distributed environments, and the current RTI-s framework provides such routers connected in a straightforward tree topology. This approach is successful for small to medium sized simulations, but faces a number of significant limitations for very large simulations over high-latency, wide area networks. In particular, traffic is forced through a single site, drastically increasing distances messages must travel to sites not near the top of the tree. Aggregate bandwidth is limited to the bandwidth of the site hosting the top router, and failures in the upper levels of the router tree can result in widespread communications losses throughout the system. To resolve these issues, this work extends the RTI-s software router infrastructure to accommodate more sophisticated, general router topologies, including both the existing tree framework and a new generalization of the fully connected mesh topologies used in the SF Express ModSAF simulations of 100K fully interacting vehicles. The new software router objects incorporate the scalable features of the SF Express design, while optionally using low-level RTI-s objects to perform actual site-to-site communications. The (substantial) limitations of the original mesh router formalism have been eliminated, allowing fully dynamic operations. The mesh topology capabilities allow aggregate bandwidth and site-to-site latencies to match actual network performance. The heavy resource load at the root node can now be distributed across routers at the participating sites

Aerospace Medicine and Biology: A continuing bibliography, supplement 191

A bibliographical list of 182 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1979 is presented

Reinforcement Learning for UAV Attitude Control

Autopilot systems are typically composed of an "inner loop" providing stability and control, while an "outer loop" is responsible for mission-level objectives, e.g. way-point navigation. Autopilot systems for UAVs are predominately implemented using Proportional, Integral Derivative (PID) control systems, which have demonstrated exceptional performance in stable environments. However more sophisticated control is required to operate in unpredictable, and harsh environments. Intelligent flight control systems is an active area of research addressing limitations of PID control most recently through the use of reinforcement learning (RL) which has had success in other applications such as robotics. However previous work has focused primarily on using RL at the mission-level controller. In this work, we investigate the performance and accuracy of the inner control loop providing attitude control when using intelligent flight control systems trained with the state-of-the-art RL algorithms, Deep Deterministic Gradient Policy (DDGP), Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). To investigate these unknowns we first developed an open-source high-fidelity simulation environment to train a flight controller attitude control of a quadrotor through RL. We then use our environment to compare their performance to that of a PID controller to identify if using RL is appropriate in high-precision, time-critical flight control.Comment: 13 pages, 9 figure