… in a Markov decision-… reinforcement learning in humans coupling behavioral and neural
analyses. Transfer phase performance is undermined by outcome context-dependent learning …

… [2], [3] to learn a collision avoidance policy without assuming that other agents follow any
particular behavior model. This work applies to generic, decision-making agents – social norms …

… on reinforcement learning, namely the Q-learning algorithm, … in the collision avoidance
behavior decision-making stage to … and application of reinforcement learning algorithm. In Section …

… Abstract—Autonomous braking through vehicle precise decision-making and control to …
This paper proposes a deep reinforcement learning (DRL)-based autonomous braking decision-…

… -action value function Q(s, a) in reinforcement learning) was used for superposition state
updating. In … In the field of decision neuroscience, all reinforcement learning models involve the …

… based decision-making (Box 1) are well-suited to identify the specific components of
reinforcement learning and decision-… research can bridge the behavioral manifestations of SUD …

… In this paper, a deep reinforcement learning based high-level driving behavior decision…
the hidden features, and a deep reinforcement learning network that learns the optimal policy. …

… Reinforcement learning and decision-making (RLDM) provide a quantitative framework and
… the underlying processes of and interactions between multiple decision-making (eg, goal-…

… do learn to favor choice of the optimal action and those who do not. Using models of
reinforcement learning … We scanned 29 subjects while they performed the reward-based decision-…

… To cast this as a problem conducive to reinforcement learning, we use the same conventions
as a recent ACT-R model on this task (Fu & Anderson 2006). Moving into dead-ends and …