Actor-critic algorithms for reinforcement learning are achieving renewed popularity due to their good convergence properties in situations where other approaches often fail (e.g., when function approximation is involved). Interestingly, there is growing evidence that actor-critic approaches based on phasic dopamine signals play a key role in biological learning through cortical and basal gangli...

A substantial subset of Parkinson's disease (PD) patients suffers from impulse control disorders (ICDs), which are side effects of dopaminergic medication. Dopamine plays a key role in reinforcement learning processes. One class of reinforcement learning models, known as the actor-critic model, suggests that two components are involved in these reinforcement learning processes: a critic, which ...

In real life, learning is greatly speeded-up by the intervention of a teacher who gives examples, or shows, how to perform a certain task. In all this abstract, we let apart structural simpli cations of the problem by the designer which to not deal explicitely with learning. The intervention of the teacher can be realized in di erent ways: verbal explanation, demonstration, guidance, shaping th...

A novel action-dependent adaptive critic design (ACD) is developed for dynamic optimization. The proposed combination of a particle swarm optimization-based actor and a neural network critic is demonstrated through dynamic sleep scheduling of wireless sensor motes for wildlife monitoring. The objective of the sleep scheduler is to dynamically adapt the sleep duration to node's battery capacity ...

In the context of fuzzy control, antecedent parameters are used to provide a segmentation of the state space so that different regions can be modeled appropriately. In Adaptive Critic methodologies, two modules (the critic and the controller) must properly segment the state space to insure good performance. In this paper, we explore the effects of tuning antecedent parameters that are shared be...