A computer-implemented method of controlling actuators in an autonomous vehicle, the method comprising: a plurality of training sessions (110-1,..., 110-K), in which a reinforcement learning, RL, agent interacts with a simulated or real-world first environment including the autonomous vehicle, such that in each training session the environment has a different initial value and each training session yields a state-action value function Q k ( s, a) dependent on state and action, wherein a state describes vehicles surrounding the autonomous vehicle; an uncertainty evaluation including computing a variability measure for values of the plurality of state-action value functions evaluated for one or more state-action pairs corresponding to possible tactical driving decisions by the trained RL agent, wherein: - each said tactical driving decision, such as a lane change or whether to stop or go at an intersection, relates to control of the autonomous vehicle in the real world; - each said tactical driving decision is based on a common state-action value function Q ( s, a) obtained by combining the plurality of state-action value functions Q k ( s, a) from the training sessions; and - the variability measure is one or more of the following: a variance, a range, a deviation, a variation coefficient, an entropy; additional training, in which the RL agent interacts with a simulated or real-world second environment including the autonomous vehicle, wherein the second environment differs from the first environment by an increased exposure to a subset of state-action pairs for which the variability measure indicates a variability measure exceeding a threshold C v; and vehicle control, in which the autonomous vehicle is controlled in the real world on the basis of tactical driving decisions made by the RL agent. a plurality of training sessions (110-1,..., 110-K), in which a reinforcement learning, RL, agent interacts with a simulated or real-world first environment including the autonomous vehicle, such that in each training session the environment has a different initial value and each training session yields a state-action value function Q k ( s, a) dependent on state and action, wherein a state describes vehicles surrounding the autonomous vehicle; an uncertainty evaluation including computing a variability measure for values of the plurality of state-action value functions evaluated for one or more state-action pairs corresponding to possible tactical driving decisions by the trained RL agent, wherein: - each said tactical driving decision, such as a lane change or whether to stop or go at an intersection, relates to control of the autonomous vehicle in the real world; - each said tactical driving decision is based on a common state-action value function Q ( s, a) obtained by combining the plurality of state-action value functions Q k ( s, a) from the training sessions; and - the variability measure is one or more of the following: a variance, a range, a deviation, a variation coefficient, an entropy; - each said tactical driving decision, such as a lane change or whether to stop or go at an intersection, relates to control of the autonomous vehicle in the real world; - each said tactical driving decision is based on a common state-action value function Q ( s, a) obtained by combining the plurality of state-action value functions Q k ( s, a) from the training sessions; and - the variability measure is one or more of the following: a variance, a range, a deviation, a variation coefficient, an entropy; additional training, in which the RL agent interacts with a simulated or real-world second environment including the autonomous vehicle, wherein the second environment differs from the first environment by an increased exposure to a subset of state-action pairs for which the variability measure indicates a variability measure exceeding a threshold C v; and vehicle control, in which the autonomous vehicle is controlled in the real world on the basis of tactical driving decisions made by the RL agent.