[PDF] Top 20 Sequential Decision Task by Adaptive Reinforcement Learning Method

... a sequential adjustment and tuning process to a desirable performance manufacturing system in the fastest possible ...Markov decision process (MDP) model for ramp-up of production stations and enabling its ... See full document

... The reinforcement learning (RL) community has made great strides in designing algorithms capable of exceeding human performance on specific ...one task at the time, each new task requiring to ... See full document

... in sequential decision ...normative decision theory, we pro- ceed in Section 4 to develop our ...for task specification in RL, inverse RL and preference-based RL, and highlights an interesting ... See full document

... tal task in natural language ...a reinforcement learning (RL) approach for keyphrase generation, with an adaptive reward function that encourages a model to generate both sufficient and accu- ... See full document

... Abstract—Reinforcement Learning (RL) can solve practical sequential decision problems, even when structures of the problems are less ...some sequential decision problems ... See full document

... eration task can change the conditions of others, as evidenced by studies in corpus linguistics, and it can therefore be undesirable to treat them all as isolated ...related decision making in the areas of ... See full document

... powerful sequential algorithmic paradigm in which a problem is solved by identifying a collection of sub-problems and tackling them one by one, smallest first, using the answers to small problems to help figure ... See full document

... in sequential learning, whenever a trained model, upon training in a new task, moves abruptly in the space of parameters, effectively “forgetting” the original ...second task, these algorithms ... See full document

... of sequential decision-making, multi-aspect classification can be formu- lated as a reinforcement learning problem, with a state space S = S ϕ × Y , where × is a Cartesian ...flexible ... See full document

... response decision nowadays, this paper proposes an adaptive intrusion response decision method based on reinforcement ...on reinforcement learning, propose an attack ... See full document

... The goal of this paper is to synthesize optimal reactive strategies for systems with respect to some unknown performance criterion and in an adversarial environment such that given temporal logic specifications are ... See full document

... The proposed generalized probabilistic fuzzy reinforcement learning GPFRL method is a modified version of the actor-critic learning architecture, where uncertainty handling is enhanced b[r] ... See full document

... applying adaptive policy-based RL training. The adaptive policy-based RL means that on each frame during the training, this model is trained adaptively based on the average distance error in the previous ... See full document

... The usual approach of comparing implicit learning in a sequential reaction time task (SRT) under single-versus dual-task conditions is known to be problematic because the secondary tas[r] ... See full document

... this task to WSN, management of sensor nodes to divide the task and distribute to the individual sensor nodes, data fusion for integration the sensor readings of the individual sensor nodes into a ... See full document

... Conceptually, our approach exploits the struc- tural information of composite tasks for efficient exploration. Specifically, in order to mitigate the reward sparsity issue, we equip our agent with an evaluation module ... See full document

... inter task correlations are identified in the unsupervised way in the random matched correlations, so that the clusters labels are most ...study Sequential clustering is going to perform with the priority ... See full document

... concurrent learning of the agents when seeking higher rewards, and hence it is more dicult to estimate the value of the ...concurrent learning of the agents when seeking higher ... See full document

... Very recently, deep learning has been used for move evaluation in Go by Maddison et al. (2014) with stunning results; it beats GNUGo, a traditional search program, 97% of the time and matches the performance of ... See full document

... of task completion dialogue management have been pro- posed recently, these frameworks still have had reward sparseness ...in reinforcement learning with sparse rewards still remains for their ... See full document