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, 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; 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 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; an uncertainty estimation , including computing a variability measure for values of the plurality of state-action value functions evaluated for a state-action pair corresponding to each of the tentative tactical driving decisions; 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 estimated uncertainty, such that one of the tentative tactical driving decisions is executed only if its variability measure is less than a threshold C v , wherein the variability measure is one or more of the following: a variance, a range, a deviation, a variation coefficient, an entropy. 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, 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; 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 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; an uncertainty estimation , including computing a variability measure for values of the plurality of state-action value functions evaluated for a state-action pair corresponding to each of the tentative tactical driving decisions; 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 estimated uncertainty, such that one of the tentative tactical driving decisions is executed only if its variability measure is less than a threshold C v , wherein the variability measure is one or more of the following: a variance, a range, a deviation, a variation coefficient, an entropy. <EOS>