KEYWORDS: Neural networks, Computer simulations, Network architectures, Computer programming, Process modeling, Finite element methods, Systems modeling, Motion models, Complex systems
Recent breakthroughs in deep net processing have shown the ability to compute solutions to physics-based problems such as the three-body problem many orders-of-magnitude times faster. In this paper, we show how a deep autoencoder, trained on paths generated using a dynamical, physics-based model can generate comparable routes much faster. The autogenerated routes have all the properties of a physics-based model without the computational burden of explicitly solving the dynamical equations. This result is useful for planning and multi-agent reinforcement learning simulation purposes. In addition, the fast route planning capability may prove useful in real time situations such as collision avoidance or fast dynamic targeting response.
This paper describes our current multi-agent reinforcement learning concepts to complement or replace classic operational planning techniques. A neural planner is used to generate many possible paths. Training of the neural planner is a onetime task using a physics-based model to create the training data. The outputs of the neural planner are achievable paths. The path intersections are represented as decision waypoint nodes in a graph. The graph is interpreted as a Markov Decision Process (MDP). The resulting MDP is much faster than non-discretized spaces to train multi-agent reinforcement algorithms because only high-level decision waypoints are considered. The technique is applicable to multiple domains including air, space, land, sea, and cyber-physical domains.
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