A Reinforcement-and-Generalization Model of Sequential Effects in Identification Learning
نویسنده
چکیده
Responses in identification-learning tasks depend on events from recent trials. A model for these sequential effects is proposed, based on previous work in category learning and founded on theories of reinforcement learning and generalization. The model is compared to two other theories in their predictions of the influence of previous stimuli and previous feedback. Two experimental paradigms are introduced that allow separate assessment of these two effects. Results support the reinforcement-and-generalization model.
منابع مشابه
Interactive Learning for Sequential Decisions and Predictions
Sequential prediction problems arise commonly in many areas of robotics and information processing: e.g., predicting a sequence of actions over time to achieve a goal in a control task, interpreting an image through a sequence of local image patch classifications, or translating speech to text through an iterative decoding procedure. Learning predictors that can reliably perform such sequential...
متن کاملUsing BELBIC based optimal controller for omni-directional threewheel robots model identified by LOLIMOT
In this paper, an intelligent controller is applied to control omni-directional robots motion. First, the dynamics of the three wheel robots, as a nonlinear plant with considerable uncertainties, is identified using an efficient algorithm of training, named LoLiMoT. Then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. This emotional l...
متن کاملSample Pages to Be Followed Exactly in Preparing Scripts Generalization of Reinforcement Learning with Cmac
To implement a generalization of value functions in Adaptive Search Element (ASE)-reinforcement learning, CMAC is integrated into ASE controller. ASEreinforcement learning scheme is briefly studied to discuss how CMAC is integrated into ASE controller. Neighbourhood Sequential Training concept is utilized to establish the look-up table of CMAC and to produce discrete control outputs. In compute...
متن کاملReinforcement learning based feedback control of tumor growth by limiting maximum chemo-drug dose using fuzzy logic
In this paper, a model-free reinforcement learning-based controller is designed to extract a treatment protocol because the design of a model-based controller is complex due to the highly nonlinear dynamics of cancer. The Q-learning algorithm is used to develop an optimal controller for cancer chemotherapy drug dosing. In the Q-learning algorithm, each entry of the Q-table is updated using data...
متن کاملOperation Scheduling of MGs Based on Deep Reinforcement Learning Algorithm
: In this paper, the operation scheduling of Microgrids (MGs), including Distributed Energy Resources (DERs) and Energy Storage Systems (ESSs), is proposed using a Deep Reinforcement Learning (DRL) based approach. Due to the dynamic characteristic of the problem, it firstly is formulated as a Markov Decision Process (MDP). Next, Deep Deterministic Policy Gradient (DDPG) algorithm is presented t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2009