A computer-implemented method for determining a set of policies for controlling a corresponding set of different acting entities (100, 110) which is represented by a corresponding set of agents (201, 202), wherein an acting entity comprises a control unit which is configured to take driving actions in dependence of a current state of the acting entity, the control unit of the vehicle making use of a policy for determining a driving action based on the state of the acting entity, the method comprising, at a time instant of an iterative training phase, - determining a set of actions of the corresponding set of agents (201, 202) using a corresponding set of state-action-functions, respectively; wherein the set of state-action-functions is dependent on a set of states of the corresponding set of agents (201, 202); - determining a set of updated states of the corresponding set of agents (201, 202) based on the determined set of actions, by operating the set of entities (100, 110) according to the corresponding set of actions in the context of a simulation and/or in the context of an actual operation of the set of entities (100, 110); - determining a set of instant rewards for the corresponding set of agents (201, 202) based on the set of actions and based on the set of states and/or based on the set of updated states of the corresponding set of agents (201, 202), using a corresponding set of reward functions for the corresponding set of agents (201, 202); - determining at least one empowerment term which is indicative of mutual information and/or correlation between at least one action from the set of actions and at least one updated state from the set of updated states; wherein determining at least one empowerment term comprises determining a set of transfer empowerment terms for the corresponding set of agents (201, 202); wherein a transfer empowerment term for a first agent - is indicative of mutual information and/or correlation between the action of the first agent and the updated states of one or more other agents from the set of agents (201, 202); and/or - is indicative of mutual information and/or correlation between the actions of one or more other agents from the set of agents (201, 202) and the updated state of the first agent; and - updating the set of state-action-functions using a Bellman equation for an objective function which depends on the set of instant rewards and which comprises the set of transfer of empowerment terms; wherein the set of policies for controlling the corresponding set of acting entities (100, 110) is determined based on the updated set of state-action-functions, respectively, by determining for each possible state an action which maximizes the state-action-function. - determining a set of actions of the corresponding set of agents (201, 202) using a corresponding set of state-action-functions, respectively; wherein the set of state-action-functions is dependent on a set of states of the corresponding set of agents (201, 202); - determining a set of updated states of the corresponding set of agents (201, 202) based on the determined set of actions, by operating the set of entities (100, 110) according to the corresponding set of actions in the context of a simulation and/or in the context of an actual operation of the set of entities (100, 110); - determining a set of instant rewards for the corresponding set of agents (201, 202) based on the set of actions and based on the set of states and/or based on the set of updated states of the corresponding set of agents (201, 202), using a corresponding set of reward functions for the corresponding set of agents (201, 202); - determining at least one empowerment term which is indicative of mutual information and/or correlation between at least one action from the set of actions and at least one updated state from the set of updated states; wherein determining at least one empowerment term comprises determining a set of transfer empowerment terms for the corresponding set of agents (201, 202); wherein a transfer empowerment term for a first agent - is indicative of mutual information and/or correlation between the action of the first agent and the updated states of one or more other agents from the set of agents (201, 202); and/or - is indicative of mutual information and/or correlation between the actions of one or more other agents from the set of agents (201, 202) and the updated state of the first agent; and - is indicative of mutual information and/or correlation between the action of the first agent and the updated states of one or more other agents from the set of agents (201, 202); and/or - is indicative of mutual information and/or correlation between the actions of one or more other agents from the set of agents (201, 202) and the updated state of the first agent; and - updating the set of state-action-functions using a Bellman equation for an objective function which depends on the set of instant rewards and which comprises the set of transfer of empowerment terms; wherein the set of policies for controlling the corresponding set of acting entities (100, 110) is determined based on the updated set of state-action-functions, respectively, by determining for each possible state an action which maximizes the state-action-function.