… To study this problem in the context of reinforcement learning, we assume that it is a
Markov decision process, where there is a set of possible states and a set of actions. A typical …

… Endowing a car with the ability to make tactical decisions, … The challenges arise mainly
because the other decision … on the tactical decision making, and top-level decision are proposed …

… a mixture of learning algorithms to improve their decision-… the neural substrates of
reinforcement learning during social … model-free reinforcement learning for social decision-making …

… as probability learning, presaged work in reinforcement learning and reward-based decision-…
Central to Estes’ work was the goal of explaining behavior in mathematical terms that could …

… in terms of our decision-theoretic (computational) approach to behavior change: anticipated
… the planning search through decision trees or mental models representing those quantities. …

… With the improvement of data collection and advancement in reinforcement learning
technologies, we see great potential in RL-based decision support systems to optimize treatment …

… in reinforcement learning algorithms. Suppose we were to use reinforcement learning to learn
… Learning the precise actuator commands from the single reward of whether or not we got …

… decisions is predicated upon our knowledge of the outcomes of the actions available to us.
Reinforcement learning … human learning and decision-making follow reinforcement learning …

… This paper proposed car-following method based on reinforcement learning for autonomous
vehicles decision-making. An approximator is used to approximate the value function by …

… decisions are based on prospective evaluation of potential action outcomes. Concurrently,
there has been growing attention to the role of hierarchy in decision-… reinforcement learning, …