[PDF] Top 20 A REDUCTION APPROACH TO CONSTRAINED REINFORCEMENT LEARNING

A REDUCTION APPROACH TO CONSTRAINED REINFORCEMENT LEARNING

... of reinforcement learning (RL) optimize a long-term reward subject to risk, safety, budget, diversity or other ...Though constrained RL problem has been studied to incorporate various constraints, ... See full document

16

Risk-Constrained Reinforcement Learning with Percentile Risk Criteria

... The reinforcement learning (RL) algorithms that have been designed to optimize the long-term performance of the system (expected sum of rewards/costs) seem to be suitable candidates for ad recommendation ... See full document

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Learning how to Active Learn: A Deep Reinforcement Learning Approach

... active learning has been applied to NLP tasks to minimise the expense of annotating data (Thomp- son et ...Active learning aims to reduce cost by identifying a subset of unlabelled data for anno- tation, ... See full document

11

A Geometric Approach to Multi-Criterion Reinforcement Learning

... the constrained optimization problem, where one criterion is optimized subject to explicit constraints on the oth- ...while constrained MDPs have received more extensive attention — see Altman (1999) and ... See full document

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Vol 10, No 2 (2018)

... mobile-WMSN approach requires dealing with the uncertain network conditions caused due to dynamic ...(network) learning model for DPM and power control ...state learning model (NSLM). It can be ... See full document

13

Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning

... gradient approach, as opposed to directly comparing the expected average reward at different points, is that it can be less susceptible to error in the presence of ... See full document

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Rethinking the Discount Factor in Reinforcement Learning: A Decision Theoretic Approach

... Reinforcement learning (RL) agents have traditionally been tasked with maximizing the value function of a Markov decision process (MDP), either in continuous settings, with fixed discount factor γ < 1, ... See full document

8

Improving Learning & Reducing Time: A Constrained Action-Based Reinforcement Learning Approach.

... the constrained action-based POMDP (CAPOMDP) framework by integrating the action- based constraints into the POMDP framework, to deal with a constrained action-based RL (CARL) scenario, which involves the ... See full document

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Reinforcement learning for robot navigation in constrained environments

... Most reinforcement learning methods are therefore structured around the estimate of the value-function, even if it is not strictly necessary to solve some RL ...solve reinforcement learning ... See full document

130

Unpaired Sentiment to Sentiment Translation: A Cycled Reinforcement Learning Approach

... ment learning performs badly because it is hard to guide two randomly initialized modules to teach each ...cycled reinforcement learning and pre-training achieves the better performance than using ... See full document

10

Natural Immune System Response As Complexe Adaptive System Using Learning Fuzzy Cognitive Maps

... and reinforcement learning has been proposed for analyzing natural immune system ...another approach, which is contained in same concepts inspired by the area of ... See full document

10

Deep Exploration via Randomized Value Functions

... the reinforcement learning problem presented in this example is easy to ad- ...expected learning time is ( N + 1 )/ 2 episodes, since whenever at a state that has not previously been visited, the ... See full document

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Evolutionary Function Approximation for Reinforcement Learning

... probabilistic reinforcement learning task from the field of autonomic computing (Kephart and Chess, ...a reinforcement learning task that requires effective function ...continual learning on ... See full document

41

An approach towards adaptive service composition in markets of composed services

... machine learning techniques are proposed to replace symbolic techniques ...machine learning techniques. In our work, a symbolic composition approach is responsible for composing solutions that are ... See full document

18

Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach

... supervised learning (see our supplements for a detailed overview): the resulting models thus learn to identify negation scopes from word-level annotations ...this approach suffers from inherent ... See full document

7

Determinantal Reinforcement Learning

... A DPP defines a probability distribution over the subsets from a ground set. The probability of a subset is propor- tional to the determinant of a principal submatrix of a posi- tive semidefinite matrix, where the ... See full document

8

Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach

... Multi-agent reinforcement learning is suitable to tackle the many problems such as multi-robot cooperation and cars ...swarm reinforcement learning[1] and fast adaptive learning in ... See full document

8

Advanced Machine Learning Approach: Deep Learning

... The reports conferred on top of illustrated that Deep Learning encompasses a heap of potential, however must overcome a number of challenges before changing into additional versatile tool. The interest and ... See full document

5

Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query based summarisation

... deep learning and reinforcement learning approaches used in the submit- ted ...deep learning architecture used features derived from the output of LSTM chains on word embeddings, plus features ... See full document

8

Reinforcement learning based approach for the navigation of a pipe inspection robot at sharp pipe corners

... RL-based approach together with the supplementary approach (I) and (II) are able to navigate the robot to move through various types of pipe corners which have different diameters and turning an- ... See full document

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