Policy compression for aircraft collision avoidance systems

Abstract

One approach to designing the decision making logic for an aircraft collision avoidance system is to frame the problem as Markov decision process and optimize the system using dynamic programming. The resulting strategy can be represented as a numeric table. This methodology has been used in the development of the ACAS X family of collision avoidance systems for manned and unmanned aircraft. However, due to the high dimensionality of the state space, discretizing the state variables can lead to very large tables. To improve storage efficiency, we propose two approaches for compressing the lookup table. The first approach exploits redundancy in the table. The table is decomposed into a set of lower-dimensional tables, some of which can be represented by single tables in areas where the lower-dimensional tables are identical or nearly identical with respect to a similarity metric. The second approach uses a deep neural network to learn a complex non-linear function approximation of the table. With the use of an asymmetric loss function and a gradient descent algorithm, the parameters for this network can be trained to provide very accurate estimates of values while preserving the relative preferences of the possible advisories for each state. As a result, the table can be approximately represented by only the parameters of the network, which reduces the required storage space by a factor of 1000. Simulation studies show that system performance is very similar using either compressed table representation in place of the original table. Even though the neural network was trained directly on the original table, the network surpasses the original table on the performance metrics and encounter sets evaluated here.