BACKGROUND AND OBJECTIVES Post-traumatic stress disorder (PTSD) is overrepresented among cigarette smokers. It has been hypothesized that those with PTSD smoke to alleviate negative affect and counteract deficient positive affect commonly associated with the disorder; however, limited research has examined associations between PTSD symptoms, smoking motives, and affective vulnerability factors....

See the abstract for Chapter C3. Delayed reinforcement learning (RL) concerns the solution of stochastic optimal control problems. In this section we formulate and discuss the basics of such problems. Solution methods for delayed RL will be presented in Sections C3.4 and C3.5. In these three sections we will mainly consider problems in which C3.4, C3.5 the state and control spaces are finite se...

In this paper, we address an under-represented class of learning algorithms in the study of connectionism: reinforcement learning. We first introduce these classic methods in a new formalism which highlights the particularities of implementations such as Q-Learning, QLearning with Hamming distance, Q-Learning with statistical clustering and Dyna-Q. We then present in this formalism a neural imp...

In a baseline condition, pigeons chose between an alternative that always provided food following a 30-s delay (100% reinforcement) and an alternative that provided food half of the time and blackout half of the time following 30-s delays (50% reinforcement). The different outcomes were signaled by different-colored keylights. On average, each alternative was chosen approximately equally often,...

Results from studies of observing responses have suggested that stimuli maintain observing owing to their special relationship to primary reinforcement (the conditioned-reinforcement hypothesis), and not because they predict the availability and nonavailability of reinforcement (the information hypothesis). The present article first reviews a study that challenges that conclusion and then repor...

We introduce the mathematical model for time variable reinforcement learning. The policy, the rewards or reinforcement function and the transition probabilities may depend on the progress of the time t. We prove that under certain conditions slightly changed methods of classical dynamic programming assure finding the optimal policy. For that we deduct the Bellman equation for the time variable ...

The success of any reinforcement learning (RL) application is in large part due to the design of an appropriate reinforcement function. A methodological framework to support the design of reinforcement functions has not been defined yet, and this critical and often underestimated activity is left to the ability of the RL application designer. We propose an approach to support reinforcement func...

In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...

concrete beams reinforced with glass fiber reinforced polymer (gfrp) bars exhibit large deflections and crack widths as compared with steel reinforced concrete beams due to the low modulus of elasticity of gfrp bars. different studies show that aci 318 equation for the effective moment of inertia ie does not predict deflection well for frp reinforced concrete beams. the purpose of this paper is...