Each employee’s performance is important in an organization. A way to motivate it is through the application of reinforcement theory which is developed by B. F. Skinner. One of the most commonly used methods is positive reinforcement in which one’s behavior is strengthened or increased based on consequences. This paper aims to review the impact of positive reinforcement on the performances of e...

Recent research provides new insights into amygdala contributions to positive emotion and reward. Studies of neuronal activity in the monkey amygdala and of autonomic responses mediated by the monkey amygdala show that, contrary to a widely held view, the amygdala is just as important for processing positive reward and reinforcement as it is for negative. In addition, neuropsychological studies...

The paper explores a very simple agent design method called Q-decomposition, wherein a complex agent is built from simpler subagents. Each subagent has its own reward function and runs its own reinforcement learning process. It supplies to a central arbitrator the Q-values (according to its own reward function) for each possible action. The arbitrator selects an action maximizing the sum of Q-v...

Cognitive radio (CR) has emerged as a promising technology to improve spectrum utilization. Capacity analysis is very useful in investigating the ultimate performance limits for wireless networks. Meanwhile, with increasing potential future applications for the CR systems, it is necessary to explore the limitations on their capacity in a dynamic spectrum access environment. However, due to spec...

Stochastic neurons are deployed for efficient adaptation of exploration parameters by gradient-following algorithms. The approach is evaluated in model-free temporal-difference learning using discrete actions. The advantage is in particular memory efficiency, because memorizing exploratory data is only required for starting states. Hence, if a learning problem consist of only one starting state...

Stimulus-reward coupling without attention can induce highly specific perceptual learning effects, suggesting that reward triggers selective plasticity within visual cortex. Additionally, dopamine-releasing events-temporally surrounding stimulus-reward associations-selectively enhance memory. These forms of plasticity may be evoked by selective modulation of stimulus representations during dopa...

Reinforcement learning (RL) methods usually treat reward functions as black boxes. As such, these must extensively interact with the environment in order to discover rewards and optimal policies. In most RL applications, however, users have program function and, hence, there is opportunity make visible -- show function's code agent so it can exploit internal structure learn policies a more samp...

Horizontal intracortical projections for agonist and antagonist muscles exist in the primary motor cortex (M1), and reward may induce a reinforcement of transmission efficiency of intracortical circuits. We investigated reward-induced change in M1 excitability for agonist and antagonist muscles. Participants were 8 healthy volunteers. Probabilistic reward tasks comprised 3 conditions of 30 tria...

Previous reports have described that neural activities in midbrain dopamine areas are sensitive to unexpected reward delivery and omission. These activities are correlated with reward prediction error in reinforcement learning models, the difference between predicted reward values and the obtained reward outcome. These findings suggest that the reward prediction error signal in the brain update...

The relationship between a reinforcement learning (RL) agent and an asynchronous environment is often ignored. Frequently used models of the interaction between an agent and its environment, such as Markov Decision Processes (MDP) or Semi-Markov Decision Processes (SMDP), do not capture the fact that, in an asynchronous environment, the state of the environment may change during computation per...