Abstract: A system and method for multi-agent reinforcement learning with periodic parameter sharing that include inputting at least one occupancy grid to a convolutional neural network (CNN) and at least one vehicle dynamic parameter into a first fully connected layer and concatenating outputs of the CNN and the first fully connected layer. The system and method also include providing Q value estimates for agent actions based on processing of the concatenated outputs and choosing at least one autonomous action to be executed by at least one of: an ego agent and a target agent. The system and method further include processing a multi-agent policy that accounts for operation of the ego agent and the target agent with respect to one another within a multi-agent environment based on the at least one autonomous action to be executed by at least one of: the ego agent and the target agent.

Abstract: A system and method for multi-agent reinforcement learning with periodic parameter sharing that include inputting at least one occupancy grid to a convolutional neural network (CNN) and at least one vehicle dynamic parameter into a first fully connected layer and concatenating outputs of the CNN and the first fully connected layer. The system and method also include providing Q value estimates for agent actions based on processing of the concatenated outputs and choosing at least one autonomous action to be executed by at least one of: an ego agent and a target agent. The system and method further include processing a multi-agent policy that accounts for operation of the ego agent and the target agent with respect to one another within a multi-agent environment based on the at least one autonomous action to be executed by at least one of: the ego agent and the target agent.