Abstract: A system and method for multi-agent reinforcement learning in a multi-agent environment that include receiving data associated with the multi-agent environment in which an ego agent and a target agent are traveling and learning a single agent policy that is based on the data associated with the multi-agent environment and that accounts for operation of at least one of: the ego agent and the target agent individually. The system and method also include learning a multi-agent policy that accounts for operation of the ego agent and the target agent with respect to one another within the multi-agent environment. The system and method further include controlling at least one of: the ego agent and the target agent to operate within the multi-agent environment based on the multi-agent policy.

Abstract: According to one aspect, uncertainty prediction based deep learning may include receiving, using a memory, a trained neural network policy ? trained based on a first dataset in a first environment, implementing, via a controller, the trained neural network policy ? in a second environment by receiving an input and generating an output y, calculating an uncertainty array U[T] for a time window T, wherein the uncertainty array is indicative of a level of uncertainty associated with an output sample distribution of the output across the time window T based on a temporal divergence, an entropy H, a variational ratio VR, and a standard deviation SD of the output y, and executing, via the controller and one or more systems, an action based on the uncertainty array U[T], such as discontinuing use of the trained neural network policy ?.