A computer-implemented method of controlling actuators in an autonomous vehicle using a reinforcement learning, RL, agent, the method comprising: a plurality of training sessions (110-1, ..., 110-K), in which the RL agent interacts with a simulated or real-world environment including the autonomous vehicle, each training session having a different initial value and yielding a state-action quantile function Z k , τ s a = F Z k s a − 1 τ dependent on state s and action a; decision-making , in which the RL agent outputs, for a state which describes the surrounding vehicles in a driving environment of the autonomous vehicle, at least one tentative tactical driving decision, such as a lane change or whether to stop or go at an intersection, relating to control of the autonomous vehicle in the real world, wherein the tentative tactical driving decision is derived from the plurality of state-action quantile functions from the training sessions; a first uncertainty estimation on the basis of a variance Var τ E k Z k , τ s a , relating to a variability with respect to quantile τ , of an average E k Z k , τ s a of the plurality of state-action quantile functions evaluated for a state-action pair corresponding to the tentative tactical driving decision; a second uncertainty estimation on the basis of a variance Var k E τ Z k , τ s a , relating to an ensemble variability, for the plurality of state-action quantile functions evaluated for a state-action pair corresponding to the tentative tactical driving decision; and vehicle control , wherein the at least one tentative tactical driving decision is executed in the real world by transmitting wired or wireless signals to the actuators in the vehicle and in dependence of the first and/or second estimated uncertainty. a plurality of training sessions (110-1, ..., 110-K), in which the RL agent interacts with a simulated or real-world environment including the autonomous vehicle, each training session having a different initial value and yielding a state-action quantile function Z k , τ s a = F Z k s a − 1 τ dependent on state s and action a; decision-making , in which the RL agent outputs, for a state which describes the surrounding vehicles in a driving environment of the autonomous vehicle, at least one tentative tactical driving decision, such as a lane change or whether to stop or go at an intersection, relating to control of the autonomous vehicle in the real world, wherein the tentative tactical driving decision is derived from the plurality of state-action quantile functions from the training sessions; a first uncertainty estimation on the basis of a variance Var τ E k Z k , τ s a , relating to a variability with respect to quantile τ , of an average E k Z k , τ s a of the plurality of state-action quantile functions evaluated for a state-action pair corresponding to the tentative tactical driving decision; a second uncertainty estimation on the basis of a variance Var k E τ Z k , τ s a , relating to an ensemble variability, for the plurality of state-action quantile functions evaluated for a state-action pair corresponding to the tentative tactical driving decision; and vehicle control , wherein the at least one tentative tactical driving decision is executed in the real world by transmitting wired or wireless signals to the actuators in the vehicle and in dependence of the first and/or second estimated uncertainty. <EOS>